Overview

Brought to you by YData

Dataset statistics

Number of variables45
Number of observations10000
Missing cells47584
Missing cells (%)10.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory360.0 B

Variable types

Text6
Categorical16
Boolean7
Numeric13
Unsupported3

Alerts

Occupancy has 3282 (32.8%) missing values Missing
Flood has 3409 (34.1%) missing values Missing
Highway has 3398 (34.0%) missing values Missing
Train has 3331 (33.3%) missing values Missing
HTW has 3401 (34.0%) missing values Missing
Pool has 3415 (34.2%) missing values Missing
Commercial has 3284 (32.8%) missing values Missing
Water has 3303 (33.0%) missing values Missing
Sewage has 3252 (32.5%) missing values Missing
Parking has 2489 (24.9%) missing values Missing
BasementYesNo has 3365 (33.7%) missing values Missing
Layout has 2554 (25.5%) missing values Missing
Rent_Restricted has 3299 (33.0%) missing values Missing
Selling_Reason has 2489 (24.9%) missing values Missing
Seller_Retained_Broker has 3313 (33.1%) missing values Missing
Property_Title has unique values Unique
Address has unique values Unique
Street_Address has unique values Unique
Longitude has unique values Unique
Valuation is an unsupported type, check if it needs cleaning or further analysis Unsupported
HOA is an unsupported type, check if it needs cleaning or further analysis Unsupported
Rehab is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-07-17 13:04:20.399951
Analysis finished2025-07-17 13:05:13.293400
Duration52.89 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Property_Title
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:13.556895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length64
Median length56
Mean length46.4421
Min length32

Characters and Unicode

Total characters464421
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row875 Davis Overpass Suite 394, South Kathrynside, CO 35116
2nd row1159 Johnson Pass Apt. 567, South Jamesfurt, MN 60391
3rd row9082 Anna Villages Apt. 511, Port Juanshire, VI 22393
4th row86578 Lawson Park Suite 865, South Brianfurt, DC 90366
5th row309 Roy Brook Apt. 282, Lake Scott, FL 84478
ValueCountFrequency (%)
suite 2500
 
3.3%
apt 2443
 
3.3%
port 790
 
1.1%
lake 772
 
1.0%
north 731
 
1.0%
west 728
 
1.0%
new 723
 
1.0%
south 720
 
1.0%
east 718
 
1.0%
ct 202
 
0.3%
Other values (23027) 64609
86.2%
2025-07-17T18:35:14.050706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64936
 
14.0%
e 25678
 
5.5%
a 20473
 
4.4%
, 20000
 
4.3%
t 19387
 
4.2%
r 18507
 
4.0%
i 15882
 
3.4%
o 15319
 
3.3%
n 14986
 
3.2%
s 13051
 
2.8%
Other values (55) 236202
50.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 212273
45.7%
Decimal Number 104776
22.6%
Space Separator 64936
 
14.0%
Uppercase Letter 59993
 
12.9%
Other Punctuation 22443
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 25678
12.1%
a 20473
9.6%
t 19387
9.1%
r 18507
 
8.7%
i 15882
 
7.5%
o 15319
 
7.2%
n 14986
 
7.1%
s 13051
 
6.1%
l 11012
 
5.2%
h 9716
 
4.6%
Other values (16) 48262
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 6721
 
11.2%
A 6158
 
10.3%
M 4811
 
8.0%
N 3670
 
6.1%
C 3645
 
6.1%
P 3097
 
5.2%
L 2858
 
4.8%
R 2800
 
4.7%
W 2748
 
4.6%
J 2525
 
4.2%
Other values (16) 20960
34.9%
Decimal Number
ValueCountFrequency (%)
1 10723
10.2%
8 10534
10.1%
7 10516
10.0%
5 10497
10.0%
2 10463
10.0%
3 10459
10.0%
9 10443
10.0%
4 10436
10.0%
6 10419
9.9%
0 10286
9.8%
Other Punctuation
ValueCountFrequency (%)
, 20000
89.1%
. 2443
 
10.9%
Space Separator
ValueCountFrequency (%)
64936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 272266
58.6%
Common 192155
41.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 25678
 
9.4%
a 20473
 
7.5%
t 19387
 
7.1%
r 18507
 
6.8%
i 15882
 
5.8%
o 15319
 
5.6%
n 14986
 
5.5%
s 13051
 
4.8%
l 11012
 
4.0%
h 9716
 
3.6%
Other values (42) 108255
39.8%
Common
ValueCountFrequency (%)
64936
33.8%
, 20000
 
10.4%
1 10723
 
5.6%
8 10534
 
5.5%
7 10516
 
5.5%
5 10497
 
5.5%
2 10463
 
5.4%
3 10459
 
5.4%
9 10443
 
5.4%
4 10436
 
5.4%
Other values (3) 23148
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 464421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64936
 
14.0%
e 25678
 
5.5%
a 20473
 
4.4%
, 20000
 
4.3%
t 19387
 
4.2%
r 18507
 
4.0%
i 15882
 
3.4%
o 15319
 
3.3%
n 14986
 
3.2%
s 13051
 
2.8%
Other values (55) 236202
50.9%

Address
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:14.356434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length64
Median length56
Mean length46.4421
Min length32

Characters and Unicode

Total characters464421
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row875 Davis Overpass Suite 394, South Kathrynside, CO 35116
2nd row1159 Johnson Pass Apt. 567, South Jamesfurt, MN 60391
3rd row9082 Anna Villages Apt. 511, Port Juanshire, VI 22393
4th row86578 Lawson Park Suite 865, South Brianfurt, DC 90366
5th row309 Roy Brook Apt. 282, Lake Scott, FL 84478
ValueCountFrequency (%)
suite 2500
 
3.3%
apt 2443
 
3.3%
port 790
 
1.1%
lake 772
 
1.0%
north 731
 
1.0%
west 728
 
1.0%
new 723
 
1.0%
south 720
 
1.0%
east 718
 
1.0%
ct 202
 
0.3%
Other values (23027) 64609
86.2%
2025-07-17T18:35:14.868917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64936
 
14.0%
e 25678
 
5.5%
a 20473
 
4.4%
, 20000
 
4.3%
t 19387
 
4.2%
r 18507
 
4.0%
i 15882
 
3.4%
o 15319
 
3.3%
n 14986
 
3.2%
s 13051
 
2.8%
Other values (55) 236202
50.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 212273
45.7%
Decimal Number 104776
22.6%
Space Separator 64936
 
14.0%
Uppercase Letter 59993
 
12.9%
Other Punctuation 22443
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 25678
12.1%
a 20473
9.6%
t 19387
9.1%
r 18507
 
8.7%
i 15882
 
7.5%
o 15319
 
7.2%
n 14986
 
7.1%
s 13051
 
6.1%
l 11012
 
5.2%
h 9716
 
4.6%
Other values (16) 48262
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 6721
 
11.2%
A 6158
 
10.3%
M 4811
 
8.0%
N 3670
 
6.1%
C 3645
 
6.1%
P 3097
 
5.2%
L 2858
 
4.8%
R 2800
 
4.7%
W 2748
 
4.6%
J 2525
 
4.2%
Other values (16) 20960
34.9%
Decimal Number
ValueCountFrequency (%)
1 10723
10.2%
8 10534
10.1%
7 10516
10.0%
5 10497
10.0%
2 10463
10.0%
3 10459
10.0%
9 10443
10.0%
4 10436
10.0%
6 10419
9.9%
0 10286
9.8%
Other Punctuation
ValueCountFrequency (%)
, 20000
89.1%
. 2443
 
10.9%
Space Separator
ValueCountFrequency (%)
64936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 272266
58.6%
Common 192155
41.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 25678
 
9.4%
a 20473
 
7.5%
t 19387
 
7.1%
r 18507
 
6.8%
i 15882
 
5.8%
o 15319
 
5.6%
n 14986
 
5.5%
s 13051
 
4.8%
l 11012
 
4.0%
h 9716
 
3.6%
Other values (42) 108255
39.8%
Common
ValueCountFrequency (%)
64936
33.8%
, 20000
 
10.4%
1 10723
 
5.6%
8 10534
 
5.5%
7 10516
 
5.5%
5 10497
 
5.5%
2 10463
 
5.4%
3 10459
 
5.4%
9 10443
 
5.4%
4 10436
 
5.4%
Other values (3) 23148
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 464421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64936
 
14.0%
e 25678
 
5.5%
a 20473
 
4.4%
, 20000
 
4.3%
t 19387
 
4.2%
r 18507
 
4.0%
i 15882
 
3.4%
o 15319
 
3.3%
n 14986
 
3.2%
s 13051
 
2.8%
Other values (55) 236202
50.9%

Reviewed_Status
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Closed
1059 
Neww
1040 
New
1009 
Cancelled
1004 
Accepted Offer
1003 
Other values (5)
4885 

Length

Max length14
Median length8
Mean length6.309
Min length1

Characters and Unicode

Total characters63090
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd rowReviewed
3rd rowNull
4th row Closed
5th row Closed

Common Values

ValueCountFrequency (%)
Closed 1059
10.6%
Neww 1040
10.4%
New 1009
10.1%
Cancelled 1004
10.0%
Accepted Offer 1003
10.0%
996
10.0%
Reviewed 995
10.0%
Offered 980
9.8%
Closed 970
9.7%
Null 944
9.4%

Length

2025-07-17T18:35:15.044667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:15.216893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
closed 2029
20.3%
neww 1040
10.4%
new 1009
10.1%
cancelled 1004
10.0%
accepted 1003
10.0%
offer 1003
10.0%
reviewed 995
9.9%
offered 980
9.8%
null 944
9.4%

Most occurring characters

ValueCountFrequency (%)
e 14040
22.3%
d 6011
9.5%
l 5925
 
9.4%
w 4084
 
6.5%
f 3966
 
6.3%
3058
 
4.8%
C 3033
 
4.8%
c 3010
 
4.8%
N 2993
 
4.7%
o 2029
 
3.2%
Other values (12) 14941
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50025
79.3%
Uppercase Letter 10007
 
15.9%
Space Separator 3058
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14040
28.1%
d 6011
12.0%
l 5925
11.8%
w 4084
 
8.2%
f 3966
 
7.9%
c 3010
 
6.0%
o 2029
 
4.1%
s 2029
 
4.1%
r 1983
 
4.0%
a 1004
 
2.0%
Other values (6) 5944
11.9%
Uppercase Letter
ValueCountFrequency (%)
C 3033
30.3%
N 2993
29.9%
O 1983
19.8%
A 1003
 
10.0%
R 995
 
9.9%
Space Separator
ValueCountFrequency (%)
3058
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60032
95.2%
Common 3058
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14040
23.4%
d 6011
10.0%
l 5925
9.9%
w 4084
 
6.8%
f 3966
 
6.6%
C 3033
 
5.1%
c 3010
 
5.0%
N 2993
 
5.0%
o 2029
 
3.4%
s 2029
 
3.4%
Other values (11) 12912
21.5%
Common
ValueCountFrequency (%)
3058
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63090
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14040
22.3%
d 6011
9.5%
l 5925
 
9.4%
w 4084
 
6.5%
f 3966
 
6.3%
3058
 
4.8%
C 3033
 
4.8%
c 3010
 
4.8%
N 2993
 
4.7%
o 2029
 
3.2%
Other values (12) 14941
23.7%
Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Null
888 
Pending
867 
Pend
861 
Cancel
840 
Always Kick
839 
Other values (7)
5705 

Length

Max length11
Median length7
Mean length6.0711
Min length1

Characters and Unicode

Total characters60711
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClosed
2nd rowCancelled
3rd rowNull
4th rowCancelled
5th rowCancelled

Common Values

ValueCountFrequency (%)
Null 888
8.9%
Pending 867
8.7%
Pend 861
8.6%
Cancel 840
8.4%
Always Kick 839
8.4%
833
8.3%
Available 831
8.3%
Close 829
8.3%
Closed 826
8.3%
Cancelled 810
8.1%
Other values (2) 1576
15.8%

Length

2025-07-17T18:35:15.414708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kick 1625
16.2%
close 1619
16.2%
null 888
8.9%
pending 867
8.7%
pend 861
8.6%
cancel 840
8.4%
always 839
8.4%
available 831
8.3%
closed 826
8.3%
cancelled 810
8.1%

Most occurring characters

ValueCountFrequency (%)
l 9182
15.1%
e 7464
12.3%
n 4245
 
7.0%
a 4151
 
6.8%
C 4095
 
6.7%
d 3364
 
5.5%
i 3323
 
5.5%
s 3284
 
5.4%
c 3275
 
5.4%
3252
 
5.4%
Other values (12) 15076
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47453
78.2%
Uppercase Letter 10006
 
16.5%
Space Separator 3252
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 9182
19.3%
e 7464
15.7%
n 4245
8.9%
a 4151
8.7%
d 3364
 
7.1%
i 3323
 
7.0%
s 3284
 
6.9%
c 3275
 
6.9%
o 2445
 
5.2%
k 1625
 
3.4%
Other values (6) 5095
10.7%
Uppercase Letter
ValueCountFrequency (%)
C 4095
40.9%
P 1728
17.3%
A 1670
16.7%
K 1625
 
16.2%
N 888
 
8.9%
Space Separator
ValueCountFrequency (%)
3252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57459
94.6%
Common 3252
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 9182
16.0%
e 7464
13.0%
n 4245
 
7.4%
a 4151
 
7.2%
C 4095
 
7.1%
d 3364
 
5.9%
i 3323
 
5.8%
s 3284
 
5.7%
c 3275
 
5.7%
o 2445
 
4.3%
Other values (11) 12631
22.0%
Common
ValueCountFrequency (%)
3252
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 9182
15.1%
e 7464
12.3%
n 4245
 
7.0%
a 4151
 
6.8%
C 4095
 
6.7%
d 3364
 
5.5%
i 3323
 
5.5%
s 3284
 
5.4%
c 3275
 
5.4%
3252
 
5.4%
Other values (12) 15076
24.8%

Source
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
M L S
1275 
Opendoor
1274 
Bulk
1272 
Internal
1263 
Auction.com
1247 
Other values (3)
3669 

Length

Max length11
Median length8
Mean length6.5018
Min length4

Characters and Unicode

Total characters65018
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInternal
2nd rowAuction.com
3rd rowOpendoor
4th rowM.L.S.
5th rowM.L.S.

Common Values

ValueCountFrequency (%)
M L S 1275
12.8%
Opendoor 1274
12.7%
Bulk 1272
12.7%
Internal 1263
12.6%
Auction.com 1247
12.5%
M.L.S. 1240
12.4%
MLS 1236
12.4%
Broker 1193
11.9%

Length

2025-07-17T18:35:15.578952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:15.738529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
m 1275
10.2%
l 1275
10.2%
s 1275
10.2%
opendoor 1274
10.2%
bulk 1272
10.1%
internal 1263
10.1%
auction.com 1247
9.9%
m.l.s 1240
9.9%
mls 1236
9.8%
broker 1193
9.5%

Most occurring characters

ValueCountFrequency (%)
o 6235
 
9.6%
n 5047
 
7.8%
. 4967
 
7.6%
r 4923
 
7.6%
3786
 
5.8%
M 3751
 
5.8%
L 3751
 
5.8%
S 3751
 
5.8%
e 3730
 
5.7%
l 2535
 
3.9%
Other values (13) 22542
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38763
59.6%
Uppercase Letter 17502
26.9%
Other Punctuation 4967
 
7.6%
Space Separator 3786
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 6235
16.1%
n 5047
13.0%
r 4923
12.7%
e 3730
9.6%
l 2535
6.5%
u 2519
6.5%
t 2510
6.5%
c 2494
 
6.4%
k 2465
 
6.4%
p 1274
 
3.3%
Other values (4) 5031
13.0%
Uppercase Letter
ValueCountFrequency (%)
M 3751
21.4%
L 3751
21.4%
S 3751
21.4%
B 2465
14.1%
O 1274
 
7.3%
I 1263
 
7.2%
A 1247
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 4967
100.0%
Space Separator
ValueCountFrequency (%)
3786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56265
86.5%
Common 8753
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 6235
 
11.1%
n 5047
 
9.0%
r 4923
 
8.7%
M 3751
 
6.7%
L 3751
 
6.7%
S 3751
 
6.7%
e 3730
 
6.6%
l 2535
 
4.5%
u 2519
 
4.5%
t 2510
 
4.5%
Other values (11) 17513
31.1%
Common
ValueCountFrequency (%)
. 4967
56.7%
3786
43.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 6235
 
9.6%
n 5047
 
7.8%
. 4967
 
7.6%
r 4923
 
7.6%
3786
 
5.8%
M 3751
 
5.8%
L 3751
 
5.8%
S 3751
 
5.8%
e 3730
 
5.7%
l 2535
 
3.9%
Other values (13) 22542
34.7%

Market
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Chicgo
1288 
Chicago
1277 
Atlanta
1261 
Florida
1254 
1245 
Other values (3)
3675 

Length

Max length7
Median length6
Mean length5.5118
Min length1

Characters and Unicode

Total characters55118
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChicago
2nd rowDallas
3rd rowTampa
4th row
5th rowTampa

Common Values

ValueCountFrequency (%)
Chicgo 1288
12.9%
Chicago 1277
12.8%
Atlanta 1261
12.6%
Florida 1254
12.5%
1245
12.4%
Tampa 1229
12.3%
Dallas 1226
12.3%
Dalas 1220
12.2%

Length

2025-07-17T18:35:15.918262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:16.064496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
chicgo 1288
14.7%
chicago 1277
14.6%
atlanta 1261
14.4%
florida 1254
14.3%
tampa 1229
14.0%
dallas 1226
14.0%
dalas 1220
13.9%

Most occurring characters

ValueCountFrequency (%)
a 12403
22.5%
l 6187
11.2%
i 3819
 
6.9%
o 3819
 
6.9%
c 2565
 
4.7%
h 2565
 
4.7%
C 2565
 
4.7%
g 2565
 
4.7%
t 2522
 
4.6%
D 2446
 
4.4%
Other values (10) 13662
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45118
81.9%
Uppercase Letter 8755
 
15.9%
Space Separator 1245
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12403
27.5%
l 6187
13.7%
i 3819
 
8.5%
o 3819
 
8.5%
c 2565
 
5.7%
h 2565
 
5.7%
g 2565
 
5.7%
t 2522
 
5.6%
s 2446
 
5.4%
n 1261
 
2.8%
Other values (4) 4966
11.0%
Uppercase Letter
ValueCountFrequency (%)
C 2565
29.3%
D 2446
27.9%
A 1261
14.4%
F 1254
14.3%
T 1229
14.0%
Space Separator
ValueCountFrequency (%)
1245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53873
97.7%
Common 1245
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12403
23.0%
l 6187
11.5%
i 3819
 
7.1%
o 3819
 
7.1%
c 2565
 
4.8%
h 2565
 
4.8%
C 2565
 
4.8%
g 2565
 
4.8%
t 2522
 
4.7%
D 2446
 
4.5%
Other values (9) 12417
23.0%
Common
ValueCountFrequency (%)
1245
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12403
22.5%
l 6187
11.2%
i 3819
 
6.9%
o 3819
 
6.9%
c 2565
 
4.7%
h 2565
 
4.7%
C 2565
 
4.7%
g 2565
 
4.7%
t 2522
 
4.6%
D 2446
 
4.4%
Other values (10) 13662
24.8%

Occupancy
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3282
Missing (%)32.8%
Memory size19.7 KiB
True
3424 
False
3294 
(Missing)
3282 
ValueCountFrequency (%)
True 3424
34.2%
False 3294
32.9%
(Missing) 3282
32.8%
2025-07-17T18:35:16.214113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Flood
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3409
Missing (%)34.1%
Memory size78.3 KiB
Minimal Flood
3316 
Flood Zone
3275 

Length

Max length13
Median length13
Mean length11.509331
Min length10

Characters and Unicode

Total characters75858
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMinimal Flood
2nd rowFlood Zone
3rd rowFlood Zone
4th rowFlood Zone
5th rowFlood Zone

Common Values

ValueCountFrequency (%)
Minimal Flood 3316
33.2%
Flood Zone 3275
32.8%
(Missing) 3409
34.1%

Length

2025-07-17T18:35:16.345930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:16.469126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
flood 6591
50.0%
minimal 3316
25.2%
zone 3275
24.8%

Most occurring characters

ValueCountFrequency (%)
o 16457
21.7%
l 9907
13.1%
i 6632
8.7%
n 6591
8.7%
d 6591
8.7%
6591
8.7%
F 6591
8.7%
M 3316
 
4.4%
m 3316
 
4.4%
a 3316
 
4.4%
Other values (2) 6550
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56085
73.9%
Uppercase Letter 13182
 
17.4%
Space Separator 6591
 
8.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 16457
29.3%
l 9907
17.7%
i 6632
11.8%
n 6591
11.8%
d 6591
11.8%
m 3316
 
5.9%
a 3316
 
5.9%
e 3275
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
F 6591
50.0%
M 3316
25.2%
Z 3275
24.8%
Space Separator
ValueCountFrequency (%)
6591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69267
91.3%
Common 6591
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 16457
23.8%
l 9907
14.3%
i 6632
9.6%
n 6591
9.5%
d 6591
9.5%
F 6591
9.5%
M 3316
 
4.8%
m 3316
 
4.8%
a 3316
 
4.8%
Z 3275
 
4.7%
Common
ValueCountFrequency (%)
6591
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 16457
21.7%
l 9907
13.1%
i 6632
8.7%
n 6591
8.7%
d 6591
8.7%
6591
8.7%
F 6591
8.7%
M 3316
 
4.4%
m 3316
 
4.4%
a 3316
 
4.4%
Other values (2) 6550
 
8.6%

Street_Address
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:16.725905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length30
Mean length22.4001
Min length10

Characters and Unicode

Total characters224001
Distinct characters63
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row875 Davis Overpass Suite 394
2nd row1159 Johnson Pass Apt. 567
3rd row9082 Anna Villages Apt. 511
4th row86578 Lawson Park Suite 865
5th row309 Roy Brook Apt. 282
ValueCountFrequency (%)
suite 2500
 
6.3%
apt 2443
 
6.1%
michael 131
 
0.3%
smith 124
 
0.3%
turnpike 111
 
0.3%
square 107
 
0.3%
drive 106
 
0.3%
inlet 102
 
0.3%
mountain 99
 
0.2%
street 98
 
0.2%
Other values (8684) 34065
85.4%
2025-07-17T18:35:17.170529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29886
 
13.3%
e 13974
 
6.2%
a 11151
 
5.0%
i 9973
 
4.5%
t 9888
 
4.4%
r 8956
 
4.0%
n 8531
 
3.8%
s 7463
 
3.3%
o 7069
 
3.2%
l 6657
 
3.0%
Other values (53) 110453
49.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111953
50.0%
Decimal Number 54776
24.5%
Space Separator 29886
 
13.3%
Uppercase Letter 24943
 
11.1%
Other Punctuation 2443
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13974
12.5%
a 11151
10.0%
i 9973
8.9%
t 9888
8.8%
r 8956
 
8.0%
n 8531
 
7.6%
s 7463
 
6.7%
o 7069
 
6.3%
l 6657
 
5.9%
u 4997
 
4.5%
Other values (16) 23294
20.8%
Uppercase Letter
ValueCountFrequency (%)
S 4439
17.8%
A 3131
12.6%
C 1863
 
7.5%
M 1800
 
7.2%
R 1463
 
5.9%
P 1408
 
5.6%
J 1088
 
4.4%
L 1016
 
4.1%
T 989
 
4.0%
B 967
 
3.9%
Other values (15) 6779
27.2%
Decimal Number
ValueCountFrequency (%)
1 5686
10.4%
7 5535
10.1%
8 5532
10.1%
2 5522
10.1%
6 5467
10.0%
9 5457
10.0%
5 5456
10.0%
3 5404
9.9%
4 5368
9.8%
0 5349
9.8%
Space Separator
ValueCountFrequency (%)
29886
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2443
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 136896
61.1%
Common 87105
38.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13974
 
10.2%
a 11151
 
8.1%
i 9973
 
7.3%
t 9888
 
7.2%
r 8956
 
6.5%
n 8531
 
6.2%
s 7463
 
5.5%
o 7069
 
5.2%
l 6657
 
4.9%
u 4997
 
3.7%
Other values (41) 48237
35.2%
Common
ValueCountFrequency (%)
29886
34.3%
1 5686
 
6.5%
7 5535
 
6.4%
8 5532
 
6.4%
2 5522
 
6.3%
6 5467
 
6.3%
9 5457
 
6.3%
5 5456
 
6.3%
3 5404
 
6.2%
4 5368
 
6.2%
Other values (2) 7792
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 224001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29886
 
13.3%
e 13974
 
6.2%
a 11151
 
5.0%
i 9973
 
4.5%
t 9888
 
4.4%
r 8956
 
4.0%
n 8531
 
3.8%
s 7463
 
3.3%
o 7069
 
3.2%
l 6657
 
3.0%
Other values (53) 110453
49.3%

City
Text

Distinct7632
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:17.427728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length20
Mean length12.042
Min length6

Characters and Unicode

Total characters120420
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6173 ?
Unique (%)61.7%

Sample

1st rowSouth Kathrynside
2nd rowSouth Jamesfurt
3rd rowPort Juanshire
4th rowSouth Brianfurt
5th rowLake Scott
ValueCountFrequency (%)
north 731
 
4.9%
new 723
 
4.8%
lake 722
 
4.8%
west 720
 
4.8%
south 720
 
4.8%
east 718
 
4.8%
port 716
 
4.8%
michael 49
 
0.3%
john 46
 
0.3%
david 41
 
0.3%
Other values (5558) 9864
65.5%
2025-07-17T18:35:17.894748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 11704
 
9.7%
r 9551
 
7.9%
t 9499
 
7.9%
a 9322
 
7.7%
o 8250
 
6.9%
h 6763
 
5.6%
n 6455
 
5.4%
i 5909
 
4.9%
s 5588
 
4.6%
5050
 
4.2%
Other values (42) 42329
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100320
83.3%
Uppercase Letter 15050
 
12.5%
Space Separator 5050
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11704
11.7%
r 9551
9.5%
t 9499
9.5%
a 9322
9.3%
o 8250
 
8.2%
h 6763
 
6.7%
n 6455
 
6.4%
i 5909
 
5.9%
s 5588
 
5.6%
l 4355
 
4.3%
Other values (16) 22924
22.9%
Uppercase Letter
ValueCountFrequency (%)
N 1636
10.9%
S 1510
 
10.0%
J 1261
 
8.4%
L 1143
 
7.6%
W 1049
 
7.0%
E 1036
 
6.9%
P 1016
 
6.8%
M 987
 
6.6%
C 784
 
5.2%
A 768
 
5.1%
Other values (15) 3860
25.6%
Space Separator
ValueCountFrequency (%)
5050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 115370
95.8%
Common 5050
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11704
 
10.1%
r 9551
 
8.3%
t 9499
 
8.2%
a 9322
 
8.1%
o 8250
 
7.2%
h 6763
 
5.9%
n 6455
 
5.6%
i 5909
 
5.1%
s 5588
 
4.8%
l 4355
 
3.8%
Other values (41) 37974
32.9%
Common
ValueCountFrequency (%)
5050
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11704
 
9.7%
r 9551
 
7.9%
t 9499
 
7.9%
a 9322
 
7.7%
o 8250
 
6.9%
h 6763
 
5.6%
n 6455
 
5.4%
i 5909
 
4.9%
s 5588
 
4.6%
5050
 
4.2%
Other values (42) 42329
35.2%

State
Text

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:18.116871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters20000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCO
2nd rowMN
3rd rowVI
4th rowDC
5th rowFL
ValueCountFrequency (%)
nd 202
 
2.0%
ct 202
 
2.0%
ia 197
 
2.0%
ar 196
 
2.0%
ok 191
 
1.9%
wy 191
 
1.9%
fl 187
 
1.9%
me 187
 
1.9%
pa 182
 
1.8%
vt 182
 
1.8%
Other values (49) 8083
80.8%
2025-07-17T18:35:18.488816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2259
 
11.3%
M 2024
 
10.1%
N 1810
 
9.0%
I 1469
 
7.3%
D 1040
 
5.2%
T 1020
 
5.1%
C 998
 
5.0%
W 877
 
4.4%
O 869
 
4.3%
V 833
 
4.2%
Other values (14) 6801
34.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2259
 
11.3%
M 2024
 
10.1%
N 1810
 
9.0%
I 1469
 
7.3%
D 1040
 
5.2%
T 1020
 
5.1%
C 998
 
5.0%
W 877
 
4.4%
O 869
 
4.3%
V 833
 
4.2%
Other values (14) 6801
34.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2259
 
11.3%
M 2024
 
10.1%
N 1810
 
9.0%
I 1469
 
7.3%
D 1040
 
5.2%
T 1020
 
5.1%
C 998
 
5.0%
W 877
 
4.4%
O 869
 
4.3%
V 833
 
4.2%
Other values (14) 6801
34.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2259
 
11.3%
M 2024
 
10.1%
N 1810
 
9.0%
I 1469
 
7.3%
D 1040
 
5.2%
T 1020
 
5.1%
C 998
 
5.0%
W 877
 
4.4%
O 869
 
4.3%
V 833
 
4.2%
Other values (14) 6801
34.0%

Zip
Real number (ℝ)

Distinct9501
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49962.804
Minimum507
Maximum99947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:18.669781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum507
5-th percentile5291.8
Q125042.75
median49752.5
Q374746.25
95-th percentile95139.05
Maximum99947
Range99440
Interquartile range (IQR)49703.5

Descriptive statistics

Standard deviation28776.811
Coefficient of variation (CV)0.57596469
Kurtosis-1.2035741
Mean49962.804
Median Absolute Deviation (MAD)24834
Skewness0.012870143
Sum4.9962804 × 108
Variance8.2810483 × 108
MonotonicityNot monotonic
2025-07-17T18:35:18.870553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47882 3
 
< 0.1%
6289 3
 
< 0.1%
73321 3
 
< 0.1%
26896 3
 
< 0.1%
37092 3
 
< 0.1%
36719 3
 
< 0.1%
38812 3
 
< 0.1%
95218 3
 
< 0.1%
58408 3
 
< 0.1%
93403 3
 
< 0.1%
Other values (9491) 9970
99.7%
ValueCountFrequency (%)
507 1
< 0.1%
513 1
< 0.1%
519 1
< 0.1%
533 1
< 0.1%
543 1
< 0.1%
549 1
< 0.1%
551 1
< 0.1%
578 1
< 0.1%
582 1
< 0.1%
588 1
< 0.1%
ValueCountFrequency (%)
99947 1
< 0.1%
99943 1
< 0.1%
99942 1
< 0.1%
99935 1
< 0.1%
99932 1
< 0.1%
99931 1
< 0.1%
99925 1
< 0.1%
99922 1
< 0.1%
99917 2
< 0.1%
99898 1
< 0.1%

Property_Type
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Single Family Home
2041 
Condo
2034 
SFR
1985 
Townhouse
1985 
Duplex
1955 

Length

Max length18
Median length6
Mean length8.2458
Min length3

Characters and Unicode

Total characters82458
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSFR
2nd rowSingle Family Home
3rd rowTownhouse
4th rowDuplex
5th rowTownhouse

Common Values

ValueCountFrequency (%)
Single Family Home 2041
20.4%
Condo 2034
20.3%
SFR 1985
19.9%
Townhouse 1985
19.9%
Duplex 1955
19.6%

Length

2025-07-17T18:35:19.069905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:19.219502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
single 2041
14.5%
family 2041
14.5%
home 2041
14.5%
condo 2034
14.4%
sfr 1985
14.1%
townhouse 1985
14.1%
duplex 1955
13.9%

Most occurring characters

ValueCountFrequency (%)
o 10079
 
12.2%
e 8022
 
9.7%
n 6060
 
7.3%
l 6037
 
7.3%
4082
 
5.0%
i 4082
 
5.0%
m 4082
 
5.0%
S 4026
 
4.9%
F 4026
 
4.9%
u 3940
 
4.8%
Other values (14) 28022
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60324
73.2%
Uppercase Letter 18052
 
21.9%
Space Separator 4082
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 10079
16.7%
e 8022
13.3%
n 6060
10.0%
l 6037
10.0%
i 4082
 
6.8%
m 4082
 
6.8%
u 3940
 
6.5%
g 2041
 
3.4%
y 2041
 
3.4%
a 2041
 
3.4%
Other values (6) 11899
19.7%
Uppercase Letter
ValueCountFrequency (%)
S 4026
22.3%
F 4026
22.3%
H 2041
11.3%
C 2034
11.3%
R 1985
11.0%
T 1985
11.0%
D 1955
10.8%
Space Separator
ValueCountFrequency (%)
4082
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78376
95.0%
Common 4082
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 10079
 
12.9%
e 8022
 
10.2%
n 6060
 
7.7%
l 6037
 
7.7%
i 4082
 
5.2%
m 4082
 
5.2%
S 4026
 
5.1%
F 4026
 
5.1%
u 3940
 
5.0%
g 2041
 
2.6%
Other values (13) 25981
33.1%
Common
ValueCountFrequency (%)
4082
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 10079
 
12.2%
e 8022
 
9.7%
n 6060
 
7.3%
l 6037
 
7.3%
4082
 
5.0%
i 4082
 
5.0%
m 4082
 
5.0%
S 4026
 
4.9%
F 4026
 
4.9%
u 3940
 
4.8%
Other values (14) 28022
34.0%

Highway
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3398
Missing (%)34.0%
Memory size78.3 KiB
Far
3381 
Near
3221 

Length

Max length4
Median length3
Mean length3.4878825
Min length3

Characters and Unicode

Total characters23027
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFar
2nd rowNear
3rd rowFar
4th rowNear
5th rowNear

Common Values

ValueCountFrequency (%)
Far 3381
33.8%
Near 3221
32.2%
(Missing) 3398
34.0%

Length

2025-07-17T18:35:19.394361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:19.538301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
far 3381
51.2%
near 3221
48.8%

Most occurring characters

ValueCountFrequency (%)
a 6602
28.7%
r 6602
28.7%
F 3381
14.7%
N 3221
14.0%
e 3221
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16425
71.3%
Uppercase Letter 6602
28.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6602
40.2%
r 6602
40.2%
e 3221
19.6%
Uppercase Letter
ValueCountFrequency (%)
F 3381
51.2%
N 3221
48.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 23027
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6602
28.7%
r 6602
28.7%
F 3381
14.7%
N 3221
14.0%
e 3221
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6602
28.7%
r 6602
28.7%
F 3381
14.7%
N 3221
14.0%
e 3221
14.0%

Train
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3331
Missing (%)33.3%
Memory size78.3 KiB
Near
3346 
Far
3323 

Length

Max length4
Median length4
Mean length3.5017244
Min length3

Characters and Unicode

Total characters23353
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFar
2nd rowNear
3rd rowFar
4th rowNear
5th rowNear

Common Values

ValueCountFrequency (%)
Near 3346
33.5%
Far 3323
33.2%
(Missing) 3331
33.3%

Length

2025-07-17T18:35:19.687053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:19.821141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
near 3346
50.2%
far 3323
49.8%

Most occurring characters

ValueCountFrequency (%)
a 6669
28.6%
r 6669
28.6%
N 3346
14.3%
e 3346
14.3%
F 3323
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16684
71.4%
Uppercase Letter 6669
 
28.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6669
40.0%
r 6669
40.0%
e 3346
20.1%
Uppercase Letter
ValueCountFrequency (%)
N 3346
50.2%
F 3323
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 23353
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6669
28.6%
r 6669
28.6%
N 3346
14.3%
e 3346
14.3%
F 3323
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6669
28.6%
r 6669
28.6%
N 3346
14.3%
e 3346
14.3%
F 3323
14.2%

Tax_Rate
Real number (ℝ)

Distinct251
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.259906
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:19.972005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.12
Q11.64
median2.27
Q32.89
95-th percentile3.38
Maximum3.5
Range2.5
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.72558935
Coefficient of variation (CV)0.32107059
Kurtosis-1.2068164
Mean2.259906
Median Absolute Deviation (MAD)0.63
Skewness-0.022795234
Sum22599.06
Variance0.5264799
MonotonicityNot monotonic
2025-07-17T18:35:20.164240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.81 61
 
0.6%
2.97 61
 
0.6%
2.89 57
 
0.6%
2.36 56
 
0.6%
3.46 56
 
0.6%
1.03 55
 
0.5%
3.19 55
 
0.5%
2.25 52
 
0.5%
1.94 52
 
0.5%
2.7 51
 
0.5%
Other values (241) 9444
94.4%
ValueCountFrequency (%)
1 25
0.2%
1.01 39
0.4%
1.02 33
0.3%
1.03 55
0.5%
1.04 42
0.4%
1.05 37
0.4%
1.06 27
0.3%
1.07 47
0.5%
1.08 42
0.4%
1.09 42
0.4%
ValueCountFrequency (%)
3.5 26
0.3%
3.49 34
0.3%
3.48 42
0.4%
3.47 42
0.4%
3.46 56
0.6%
3.45 42
0.4%
3.44 49
0.5%
3.43 46
0.5%
3.42 37
0.4%
3.41 39
0.4%

SQFT_Basement
Real number (ℝ)

Distinct1986
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean999.2055
Minimum0
Maximum2000
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:20.346148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile104
Q1494
median1000
Q31502
95-th percentile1899.05
Maximum2000
Range2000
Interquartile range (IQR)1008

Descriptive statistics

Standard deviation578.15143
Coefficient of variation (CV)0.57861114
Kurtosis-1.2041448
Mean999.2055
Median Absolute Deviation (MAD)503
Skewness0.0020206474
Sum9992055
Variance334259.08
MonotonicityNot monotonic
2025-07-17T18:35:20.540203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1894 14
 
0.1%
272 13
 
0.1%
1975 13
 
0.1%
1693 13
 
0.1%
1602 12
 
0.1%
1040 12
 
0.1%
1573 12
 
0.1%
446 12
 
0.1%
325 12
 
0.1%
1008 12
 
0.1%
Other values (1976) 9875
98.8%
ValueCountFrequency (%)
0 3
< 0.1%
1 3
< 0.1%
2 4
< 0.1%
3 3
< 0.1%
4 7
0.1%
5 6
0.1%
6 4
< 0.1%
7 3
< 0.1%
8 4
< 0.1%
9 7
0.1%
ValueCountFrequency (%)
2000 6
0.1%
1999 4
< 0.1%
1998 3
 
< 0.1%
1997 3
 
< 0.1%
1996 9
0.1%
1995 3
 
< 0.1%
1994 3
 
< 0.1%
1993 4
< 0.1%
1992 5
0.1%
1991 1
 
< 0.1%

HTW
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3401
Missing (%)34.0%
Memory size19.7 KiB
True
3332 
False
3267 
(Missing)
3401 
ValueCountFrequency (%)
True 3332
33.3%
False 3267
32.7%
(Missing) 3401
34.0%
2025-07-17T18:35:20.700937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Pool
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3415
Missing (%)34.2%
Memory size19.7 KiB
True
3308 
False
3277 
(Missing)
3415 
ValueCountFrequency (%)
True 3308
33.1%
False 3277
32.8%
(Missing) 3415
34.2%
2025-07-17T18:35:20.815489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Commercial
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3284
Missing (%)32.8%
Memory size19.7 KiB
False
3392 
True
3324 
(Missing)
3284 
ValueCountFrequency (%)
False 3392
33.9%
True 3324
33.2%
(Missing) 3284
32.8%
2025-07-17T18:35:20.930445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Water
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3303
Missing (%)33.0%
Memory size78.3 KiB
City
3365 
Well
3332 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters26788
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWell
2nd rowCity
3rd rowCity
4th rowCity
5th rowWell

Common Values

ValueCountFrequency (%)
City 3365
33.7%
Well 3332
33.3%
(Missing) 3303
33.0%

Length

2025-07-17T18:35:21.073110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:21.199211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
city 3365
50.2%
well 3332
49.8%

Most occurring characters

ValueCountFrequency (%)
l 6664
24.9%
i 3365
12.6%
C 3365
12.6%
t 3365
12.6%
y 3365
12.6%
W 3332
12.4%
e 3332
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20091
75.0%
Uppercase Letter 6697
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 6664
33.2%
i 3365
16.7%
t 3365
16.7%
y 3365
16.7%
e 3332
16.6%
Uppercase Letter
ValueCountFrequency (%)
C 3365
50.2%
W 3332
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 26788
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 6664
24.9%
i 3365
12.6%
C 3365
12.6%
t 3365
12.6%
y 3365
12.6%
W 3332
12.4%
e 3332
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 6664
24.9%
i 3365
12.6%
C 3365
12.6%
t 3365
12.6%
y 3365
12.6%
W 3332
12.4%
e 3332
12.4%

Sewage
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3252
Missing (%)32.5%
Memory size78.3 KiB
City
3435 
Septic
3313 

Length

Max length6
Median length4
Mean length4.9819206
Min length4

Characters and Unicode

Total characters33618
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity
2nd rowSeptic
3rd rowCity
4th rowCity
5th rowCity

Common Values

ValueCountFrequency (%)
City 3435
34.4%
Septic 3313
33.1%
(Missing) 3252
32.5%

Length

2025-07-17T18:35:21.351033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:21.490529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
city 3435
50.9%
septic 3313
49.1%

Most occurring characters

ValueCountFrequency (%)
i 6748
20.1%
t 6748
20.1%
C 3435
10.2%
y 3435
10.2%
S 3313
9.9%
e 3313
9.9%
p 3313
9.9%
c 3313
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26870
79.9%
Uppercase Letter 6748
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6748
25.1%
t 6748
25.1%
y 3435
12.8%
e 3313
12.3%
p 3313
12.3%
c 3313
12.3%
Uppercase Letter
ValueCountFrequency (%)
C 3435
50.9%
S 3313
49.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 33618
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6748
20.1%
t 6748
20.1%
C 3435
10.2%
y 3435
10.2%
S 3313
9.9%
e 3313
9.9%
p 3313
9.9%
c 3313
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6748
20.1%
t 6748
20.1%
C 3435
10.2%
y 3435
10.2%
S 3313
9.9%
e 3313
9.9%
p 3313
9.9%
c 3313
9.9%

Year_Built
Real number (ℝ)

Distinct105
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.8732
Minimum1920
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:21.640258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile1924
Q11945
median1972
Q31998
95-th percentile2019
Maximum2024
Range104
Interquartile range (IQR)53

Descriptive statistics

Standard deviation30.468672
Coefficient of variation (CV)0.015451638
Kurtosis-1.2156541
Mean1971.8732
Median Absolute Deviation (MAD)27
Skewness-0.0035910342
Sum19718732
Variance928.33996
MonotonicityNot monotonic
2025-07-17T18:35:21.823292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1958 113
 
1.1%
2006 111
 
1.1%
1931 111
 
1.1%
1948 111
 
1.1%
2003 110
 
1.1%
1938 109
 
1.1%
2001 109
 
1.1%
1922 107
 
1.1%
1952 107
 
1.1%
2014 107
 
1.1%
Other values (95) 8905
89.0%
ValueCountFrequency (%)
1920 99
1.0%
1921 105
1.1%
1922 107
1.1%
1923 104
1.0%
1924 97
1.0%
1925 89
0.9%
1926 92
0.9%
1927 97
1.0%
1928 88
0.9%
1929 92
0.9%
ValueCountFrequency (%)
2024 94
0.9%
2023 87
0.9%
2022 79
0.8%
2021 103
1.0%
2020 100
1.0%
2019 102
1.0%
2018 91
0.9%
2017 104
1.0%
2016 91
0.9%
2015 92
0.9%

SQFT_MU
Real number (ℝ)

Distinct2904
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.8484
Minimum500
Maximum3500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:21.992992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile644
Q11239
median1991
Q32758
95-th percentile3358
Maximum3500
Range3000
Interquartile range (IQR)1519

Descriptive statistics

Standard deviation873.8701
Coefficient of variation (CV)0.43762466
Kurtosis-1.21654
Mean1996.8484
Median Absolute Deviation (MAD)761.5
Skewness0.00090927372
Sum19968484
Variance763648.96
MonotonicityNot monotonic
2025-07-17T18:35:22.163066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
672 10
 
0.1%
3359 10
 
0.1%
2205 10
 
0.1%
701 10
 
0.1%
978 10
 
0.1%
868 9
 
0.1%
3073 9
 
0.1%
881 9
 
0.1%
2233 9
 
0.1%
1380 9
 
0.1%
Other values (2894) 9905
99.1%
ValueCountFrequency (%)
500 6
0.1%
501 6
0.1%
502 3
< 0.1%
503 6
0.1%
504 4
< 0.1%
505 4
< 0.1%
506 2
 
< 0.1%
507 1
 
< 0.1%
508 4
< 0.1%
509 4
< 0.1%
ValueCountFrequency (%)
3500 1
 
< 0.1%
3499 2
 
< 0.1%
3498 2
 
< 0.1%
3497 2
 
< 0.1%
3496 3
< 0.1%
3495 4
< 0.1%
3494 2
 
< 0.1%
3493 7
0.1%
3492 5
0.1%
3491 4
< 0.1%

SQFT_Total
Real number (ℝ)

Distinct5066
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6737.91
Minimum3500
Maximum9998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:22.338885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3500
5-th percentile3822
Q15111.75
median6723
Q38365
95-th percentile9666.05
Maximum9998
Range6498
Interquartile range (IQR)3253.25

Descriptive statistics

Standard deviation1879.7974
Coefficient of variation (CV)0.2789882
Kurtosis-1.2038547
Mean6737.91
Median Absolute Deviation (MAD)1623
Skewness0.0093206707
Sum67379100
Variance3533638.2
MonotonicityNot monotonic
2025-07-17T18:35:22.520504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6802 7
 
0.1%
5903 7
 
0.1%
5041 7
 
0.1%
9817 7
 
0.1%
4457 7
 
0.1%
9577 7
 
0.1%
9312 7
 
0.1%
9407 7
 
0.1%
9785 6
 
0.1%
8908 6
 
0.1%
Other values (5056) 9932
99.3%
ValueCountFrequency (%)
3500 2
 
< 0.1%
3501 5
0.1%
3502 5
0.1%
3504 2
 
< 0.1%
3505 1
 
< 0.1%
3507 1
 
< 0.1%
3508 2
 
< 0.1%
3509 2
 
< 0.1%
3510 2
 
< 0.1%
3511 1
 
< 0.1%
ValueCountFrequency (%)
9998 3
< 0.1%
9997 2
 
< 0.1%
9996 5
0.1%
9995 2
 
< 0.1%
9994 2
 
< 0.1%
9993 3
< 0.1%
9992 3
< 0.1%
9991 2
 
< 0.1%
9989 2
 
< 0.1%
9988 3
< 0.1%

Parking
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing2489
Missing (%)24.9%
Memory size78.3 KiB
Street
2523 
Garage
2517 
Driveway
2471 

Length

Max length8
Median length6
Mean length6.6579683
Min length6

Characters and Unicode

Total characters50008
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStreet
2nd rowDriveway
3rd rowDriveway
4th rowGarage
5th rowGarage

Common Values

ValueCountFrequency (%)
Street 2523
25.2%
Garage 2517
25.2%
Driveway 2471
24.7%
(Missing) 2489
24.9%

Length

2025-07-17T18:35:22.718329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:22.873922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
street 2523
33.6%
garage 2517
33.5%
driveway 2471
32.9%

Most occurring characters

ValueCountFrequency (%)
e 10034
20.1%
r 7511
15.0%
a 7505
15.0%
t 5046
10.1%
S 2523
 
5.0%
G 2517
 
5.0%
g 2517
 
5.0%
D 2471
 
4.9%
i 2471
 
4.9%
v 2471
 
4.9%
Other values (2) 4942
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42497
85.0%
Uppercase Letter 7511
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10034
23.6%
r 7511
17.7%
a 7505
17.7%
t 5046
11.9%
g 2517
 
5.9%
i 2471
 
5.8%
v 2471
 
5.8%
w 2471
 
5.8%
y 2471
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
S 2523
33.6%
G 2517
33.5%
D 2471
32.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 50008
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10034
20.1%
r 7511
15.0%
a 7505
15.0%
t 5046
10.1%
S 2523
 
5.0%
G 2517
 
5.0%
g 2517
 
5.0%
D 2471
 
4.9%
i 2471
 
4.9%
v 2471
 
4.9%
Other values (2) 4942
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10034
20.1%
r 7511
15.0%
a 7505
15.0%
t 5046
10.1%
S 2523
 
5.0%
G 2517
 
5.0%
g 2517
 
5.0%
D 2471
 
4.9%
i 2471
 
4.9%
v 2471
 
4.9%
Other values (2) 4942
9.9%

Bed
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5028
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:23.333531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7192695
Coefficient of variation (CV)0.49082721
Kurtosis-1.2836262
Mean3.5028
Median Absolute Deviation (MAD)2
Skewness-0.0040403947
Sum35028
Variance2.9558877
MonotonicityNot monotonic
2025-07-17T18:35:23.480895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1704
17.0%
6 1699
17.0%
5 1678
16.8%
3 1654
16.5%
2 1641
16.4%
4 1624
16.2%
ValueCountFrequency (%)
1 1704
17.0%
2 1641
16.4%
3 1654
16.5%
4 1624
16.2%
5 1678
16.8%
6 1699
17.0%
ValueCountFrequency (%)
6 1699
17.0%
5 1678
16.8%
4 1624
16.2%
3 1654
16.5%
2 1641
16.4%
1 1704
17.0%

Bath
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
3
2544 
4
2536 
1
2490 
2
2430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row4

Common Values

ValueCountFrequency (%)
3 2544
25.4%
4 2536
25.4%
1 2490
24.9%
2 2430
24.3%

Length

2025-07-17T18:35:23.635618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:23.763299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
3 2544
25.4%
4 2536
25.4%
1 2490
24.9%
2 2430
24.3%

Most occurring characters

ValueCountFrequency (%)
3 2544
25.4%
4 2536
25.4%
1 2490
24.9%
2 2430
24.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2544
25.4%
4 2536
25.4%
1 2490
24.9%
2 2430
24.3%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2544
25.4%
4 2536
25.4%
1 2490
24.9%
2 2430
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2544
25.4%
4 2536
25.4%
1 2490
24.9%
2 2430
24.3%

BasementYesNo
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3365
Missing (%)33.7%
Memory size19.7 KiB
False
3382 
True
3253 
(Missing)
3365 
ValueCountFrequency (%)
False 3382
33.8%
True 3253
32.5%
(Missing) 3365
33.7%
2025-07-17T18:35:23.886783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Layout
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing2554
Missing (%)25.5%
Memory size78.3 KiB
Ranch
2547 
Split
2489 
Colonial
2410 

Length

Max length8
Median length5
Mean length5.9709911
Min length5

Characters and Unicode

Total characters44460
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSplit
2nd rowColonial
3rd rowRanch
4th rowRanch
5th rowSplit

Common Values

ValueCountFrequency (%)
Ranch 2547
25.5%
Split 2489
24.9%
Colonial 2410
24.1%
(Missing) 2554
25.5%

Length

2025-07-17T18:35:24.021925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:24.157266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ranch 2547
34.2%
split 2489
33.4%
colonial 2410
32.4%

Most occurring characters

ValueCountFrequency (%)
l 7309
16.4%
a 4957
11.1%
n 4957
11.1%
i 4899
11.0%
o 4820
10.8%
R 2547
 
5.7%
c 2547
 
5.7%
h 2547
 
5.7%
p 2489
 
5.6%
S 2489
 
5.6%
Other values (2) 4899
11.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37014
83.3%
Uppercase Letter 7446
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 7309
19.7%
a 4957
13.4%
n 4957
13.4%
i 4899
13.2%
o 4820
13.0%
c 2547
 
6.9%
h 2547
 
6.9%
p 2489
 
6.7%
t 2489
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
R 2547
34.2%
S 2489
33.4%
C 2410
32.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 44460
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 7309
16.4%
a 4957
11.1%
n 4957
11.1%
i 4899
11.0%
o 4820
10.8%
R 2547
 
5.7%
c 2547
 
5.7%
h 2547
 
5.7%
p 2489
 
5.6%
S 2489
 
5.6%
Other values (2) 4899
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 7309
16.4%
a 4957
11.1%
n 4957
11.1%
i 4899
11.0%
o 4820
10.8%
R 2547
 
5.7%
c 2547
 
5.7%
h 2547
 
5.7%
p 2489
 
5.6%
S 2489
 
5.6%
Other values (2) 4899
11.0%

Net_Yield
Real number (ℝ)

Distinct1795
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.044143
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:24.305576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.89
Q16.5275
median11.06
Q315.57
95-th percentile19.07
Maximum20
Range18
Interquartile range (IQR)9.0425

Descriptive statistics

Standard deviation5.2165794
Coefficient of variation (CV)0.47233899
Kurtosis-1.2144257
Mean11.044143
Median Absolute Deviation (MAD)4.53
Skewness-0.01074962
Sum110441.43
Variance27.212701
MonotonicityNot monotonic
2025-07-17T18:35:24.487339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.55 16
 
0.2%
4.49 14
 
0.1%
5.59 14
 
0.1%
2.52 14
 
0.1%
18.87 14
 
0.1%
11.97 13
 
0.1%
6.79 13
 
0.1%
12.85 13
 
0.1%
15.92 13
 
0.1%
14.25 13
 
0.1%
Other values (1785) 9863
98.6%
ValueCountFrequency (%)
2 2
 
< 0.1%
2.01 5
0.1%
2.02 6
0.1%
2.03 6
0.1%
2.04 2
 
< 0.1%
2.05 6
0.1%
2.06 8
0.1%
2.07 4
< 0.1%
2.08 9
0.1%
2.09 7
0.1%
ValueCountFrequency (%)
20 4
< 0.1%
19.99 8
0.1%
19.98 2
 
< 0.1%
19.97 9
0.1%
19.96 4
< 0.1%
19.95 5
0.1%
19.94 9
0.1%
19.93 8
0.1%
19.92 3
 
< 0.1%
19.91 6
0.1%

IRR
Real number (ℝ)

Distinct2456
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.453593
Minimum5
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:24.679634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.18
Q111.11
median17.415
Q323.81
95-th percentile28.7105
Maximum30
Range25
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation7.2563289
Coefficient of variation (CV)0.41574986
Kurtosis-1.2172142
Mean17.453593
Median Absolute Deviation (MAD)6.355
Skewness0.006590155
Sum174535.93
Variance52.65431
MonotonicityNot monotonic
2025-07-17T18:35:24.867218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.16 13
 
0.1%
27.43 11
 
0.1%
15.16 11
 
0.1%
7.18 11
 
0.1%
20.62 11
 
0.1%
21.93 10
 
0.1%
19.53 10
 
0.1%
15.19 10
 
0.1%
27.78 10
 
0.1%
19.34 10
 
0.1%
Other values (2446) 9893
98.9%
ValueCountFrequency (%)
5 5
0.1%
5.01 3
 
< 0.1%
5.02 3
 
< 0.1%
5.03 5
0.1%
5.04 9
0.1%
5.05 7
0.1%
5.06 3
 
< 0.1%
5.07 3
 
< 0.1%
5.08 1
 
< 0.1%
5.09 4
< 0.1%
ValueCountFrequency (%)
30 3
 
< 0.1%
29.99 1
 
< 0.1%
29.96 5
0.1%
29.95 5
0.1%
29.94 7
0.1%
29.93 2
 
< 0.1%
29.92 8
0.1%
29.91 4
< 0.1%
29.9 6
0.1%
29.89 7
0.1%

Rent_Restricted
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3299
Missing (%)33.0%
Memory size19.7 KiB
False
3386 
True
3315 
(Missing)
3299 
ValueCountFrequency (%)
False 3386
33.9%
True 3315
33.1%
(Missing) 3299
33.0%
2025-07-17T18:35:25.018097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2
2044 
1
2033 
5
1996 
4
1974 
3
1953 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 2044
20.4%
1 2033
20.3%
5 1996
20.0%
4 1974
19.7%
3 1953
19.5%

Length

2025-07-17T18:35:25.152324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:25.300380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2 2044
20.4%
1 2033
20.3%
5 1996
20.0%
4 1974
19.7%
3 1953
19.5%

Most occurring characters

ValueCountFrequency (%)
2 2044
20.4%
1 2033
20.3%
5 1996
20.0%
4 1974
19.7%
3 1953
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2044
20.4%
1 2033
20.3%
5 1996
20.0%
4 1974
19.7%
3 1953
19.5%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2044
20.4%
1 2033
20.3%
5 1996
20.0%
4 1974
19.7%
3 1953
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2044
20.4%
1 2033
20.3%
5 1996
20.0%
4 1974
19.7%
3 1953
19.5%

Latitude
Real number (ℝ)

Distinct9994
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.471231
Minimum33.001189
Maximum41.999934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:25.475060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum33.001189
5-th percentile33.451369
Q135.170957
median37.486232
Q339.717343
95-th percentile41.553713
Maximum41.999934
Range8.998745
Interquartile range (IQR)4.546387

Descriptive statistics

Standard deviation2.603393
Coefficient of variation (CV)0.069477114
Kurtosis-1.2117891
Mean37.471231
Median Absolute Deviation (MAD)2.2773035
Skewness0.013525567
Sum374712.31
Variance6.777655
MonotonicityNot monotonic
2025-07-17T18:35:25.658792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.289527 2
 
< 0.1%
37.432358 2
 
< 0.1%
36.417808 2
 
< 0.1%
38.472434 2
 
< 0.1%
40.606412 2
 
< 0.1%
34.237875 2
 
< 0.1%
33.25836 1
 
< 0.1%
41.684385 1
 
< 0.1%
34.66522 1
 
< 0.1%
37.51538 1
 
< 0.1%
Other values (9984) 9984
99.8%
ValueCountFrequency (%)
33.001189 1
< 0.1%
33.001222 1
< 0.1%
33.001357 1
< 0.1%
33.001974 1
< 0.1%
33.00286 1
< 0.1%
33.00369 1
< 0.1%
33.004336 1
< 0.1%
33.005134 1
< 0.1%
33.005528 1
< 0.1%
33.007738 1
< 0.1%
ValueCountFrequency (%)
41.999934 1
< 0.1%
41.999561 1
< 0.1%
41.999198 1
< 0.1%
41.997973 1
< 0.1%
41.997056 1
< 0.1%
41.996934 1
< 0.1%
41.996878 1
< 0.1%
41.996816 1
< 0.1%
41.995877 1
< 0.1%
41.995486 1
< 0.1%

Longitude
Real number (ℝ)

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-95.57226
Minimum-117.9969
Maximum-73.00314
Zeros0
Zeros (%)0.0%
Negative10000
Negative (%)100.0%
Memory size78.3 KiB
2025-07-17T18:35:25.841316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-117.9969
5-th percentile-115.75433
Q1-106.68936
median-95.574363
Q3-84.457791
95-th percentile-75.327866
Maximum-73.00314
Range44.993756
Interquartile range (IQR)22.231568

Descriptive statistics

Standard deviation12.965364
Coefficient of variation (CV)-0.13566033
Kurtosis-1.1937509
Mean-95.57226
Median Absolute Deviation (MAD)11.11625
Skewness0.0019947383
Sum-955722.6
Variance168.10067
MonotonicityNot monotonic
2025-07-17T18:35:26.016512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-86.346937 1
 
< 0.1%
-86.90819 1
 
< 0.1%
-99.906775 1
 
< 0.1%
-78.548787 1
 
< 0.1%
-99.034369 1
 
< 0.1%
-93.287636 1
 
< 0.1%
-87.954408 1
 
< 0.1%
-92.456107 1
 
< 0.1%
-101.772359 1
 
< 0.1%
-74.911876 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-117.996896 1
< 0.1%
-117.994789 1
< 0.1%
-117.989385 1
< 0.1%
-117.98491 1
< 0.1%
-117.977826 1
< 0.1%
-117.977576 1
< 0.1%
-117.976597 1
< 0.1%
-117.974801 1
< 0.1%
-117.974707 1
< 0.1%
-117.967805 1
< 0.1%
ValueCountFrequency (%)
-73.00314 1
< 0.1%
-73.003629 1
< 0.1%
-73.007117 1
< 0.1%
-73.010278 1
< 0.1%
-73.011093 1
< 0.1%
-73.01738 1
< 0.1%
-73.018517 1
< 0.1%
-73.019663 1
< 0.1%
-73.023877 1
< 0.1%
-73.026027 1
< 0.1%
Distinct195
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:26.334508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.6779
Min length3

Characters and Unicode

Total characters56779
Distinct characters47
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManors
2nd rowCliff
3rd rowSquare
4th rowSprings
5th rowBrooks
ValueCountFrequency (%)
stream 103
 
1.0%
station 100
 
1.0%
vista 100
 
1.0%
island 98
 
1.0%
stravenue 98
 
1.0%
mountain 95
 
0.9%
squares 95
 
0.9%
islands 94
 
0.9%
drive 93
 
0.9%
port 93
 
0.9%
Other values (185) 9031
90.3%
2025-07-17T18:35:26.807017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5265
 
9.3%
a 5161
 
9.1%
s 4600
 
8.1%
r 4323
 
7.6%
o 3566
 
6.3%
n 3550
 
6.3%
i 3346
 
5.9%
l 3335
 
5.9%
t 2623
 
4.6%
u 1591
 
2.8%
Other values (37) 19419
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46779
82.4%
Uppercase Letter 10000
 
17.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5265
11.3%
a 5161
11.0%
s 4600
9.8%
r 4323
9.2%
o 3566
 
7.6%
n 3550
 
7.6%
i 3346
 
7.2%
l 3335
 
7.1%
t 2623
 
5.6%
u 1591
 
3.4%
Other values (15) 9419
20.1%
Uppercase Letter
ValueCountFrequency (%)
S 1161
11.6%
C 1137
11.4%
P 1054
10.5%
F 756
 
7.6%
R 753
 
7.5%
M 714
 
7.1%
L 599
 
6.0%
V 542
 
5.4%
T 456
 
4.6%
G 402
 
4.0%
Other values (12) 2426
24.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 56779
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5265
 
9.3%
a 5161
 
9.1%
s 4600
 
8.1%
r 4323
 
7.6%
o 3566
 
6.3%
n 3550
 
6.3%
i 3346
 
5.9%
l 3335
 
5.9%
t 2623
 
4.6%
u 1591
 
2.8%
Other values (37) 19419
34.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5265
 
9.3%
a 5161
 
9.1%
s 4600
 
8.1%
r 4323
 
7.6%
o 3566
 
6.3%
n 3550
 
6.3%
i 3346
 
5.9%
l 3335
 
5.9%
t 2623
 
4.6%
u 1591
 
2.8%
Other values (37) 19419
34.2%

Taxes
Real number (ℝ)

Distinct5884
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5731.8005
Minimum1500
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:26.975141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile1911
Q13626.75
median5739.5
Q37826.25
95-th percentile9571
Maximum10000
Range8500
Interquartile range (IQR)4199.5

Descriptive statistics

Standard deviation2449.9063
Coefficient of variation (CV)0.42742351
Kurtosis-1.1800042
Mean5731.8005
Median Absolute Deviation (MAD)2098.5
Skewness0.0065139982
Sum57318005
Variance6002041
MonotonicityNot monotonic
2025-07-17T18:35:27.145769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8160 7
 
0.1%
9171 7
 
0.1%
6081 7
 
0.1%
9696 6
 
0.1%
3540 6
 
0.1%
1704 6
 
0.1%
2230 6
 
0.1%
6416 6
 
0.1%
9169 6
 
0.1%
3932 6
 
0.1%
Other values (5874) 9937
99.4%
ValueCountFrequency (%)
1500 3
< 0.1%
1501 2
< 0.1%
1502 1
 
< 0.1%
1503 3
< 0.1%
1507 1
 
< 0.1%
1508 2
< 0.1%
1509 2
< 0.1%
1510 1
 
< 0.1%
1511 3
< 0.1%
1512 2
< 0.1%
ValueCountFrequency (%)
10000 3
< 0.1%
9999 1
 
< 0.1%
9997 1
 
< 0.1%
9996 1
 
< 0.1%
9995 3
< 0.1%
9994 4
< 0.1%
9991 1
 
< 0.1%
9990 3
< 0.1%
9989 1
 
< 0.1%
9988 4
< 0.1%

Selling_Reason
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing2489
Missing (%)24.9%
Memory size78.3 KiB
Investor Sale
2580 
Downsizing
2501 
Job Transfer
2430 

Length

Max length13
Median length12
Mean length11.67754
Min length10

Characters and Unicode

Total characters87710
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDownsizing
2nd rowJob Transfer
3rd rowInvestor Sale
4th rowDownsizing
5th rowJob Transfer

Common Values

ValueCountFrequency (%)
Investor Sale 2580
25.8%
Downsizing 2501
25.0%
Job Transfer 2430
24.3%
(Missing) 2489
24.9%

Length

2025-07-17T18:35:27.313270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:27.457723image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
investor 2580
20.6%
sale 2580
20.6%
downsizing 2501
20.0%
job 2430
19.4%
transfer 2430
19.4%

Most occurring characters

ValueCountFrequency (%)
n 10012
 
11.4%
e 7590
 
8.7%
s 7511
 
8.6%
o 7511
 
8.6%
r 7440
 
8.5%
5010
 
5.7%
a 5010
 
5.7%
i 5002
 
5.7%
I 2580
 
2.9%
v 2580
 
2.9%
Other values (11) 27464
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70179
80.0%
Uppercase Letter 12521
 
14.3%
Space Separator 5010
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 10012
14.3%
e 7590
10.8%
s 7511
10.7%
o 7511
10.7%
r 7440
10.6%
a 5010
7.1%
i 5002
7.1%
v 2580
 
3.7%
t 2580
 
3.7%
l 2580
 
3.7%
Other values (5) 12363
17.6%
Uppercase Letter
ValueCountFrequency (%)
I 2580
20.6%
S 2580
20.6%
D 2501
20.0%
J 2430
19.4%
T 2430
19.4%
Space Separator
ValueCountFrequency (%)
5010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 82700
94.3%
Common 5010
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 10012
12.1%
e 7590
 
9.2%
s 7511
 
9.1%
o 7511
 
9.1%
r 7440
 
9.0%
a 5010
 
6.1%
i 5002
 
6.0%
I 2580
 
3.1%
v 2580
 
3.1%
t 2580
 
3.1%
Other values (10) 24884
30.1%
Common
ValueCountFrequency (%)
5010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 10012
 
11.4%
e 7590
 
8.7%
s 7511
 
8.6%
o 7511
 
8.6%
r 7440
 
8.5%
5010
 
5.7%
a 5010
 
5.7%
i 5002
 
5.7%
I 2580
 
2.9%
v 2580
 
2.9%
Other values (11) 27464
31.3%

Seller_Retained_Broker
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3313
Missing (%)33.1%
Memory size19.7 KiB
False
3362 
True
3325 
(Missing)
3313 
ValueCountFrequency (%)
False 3362
33.6%
True 3325
33.2%
(Missing) 3313
33.1%
2025-07-17T18:35:27.582232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Final_Reviewer
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Emily Patel
1047 
Luis Garcia
1033 
Priya Sharma
1027 
David Thompson
1015 
Sophia Williams
1005 
Other values (5)
4873 

Length

Max length15
Median length14
Mean length12.3009
Min length10

Characters and Unicode

Total characters123009
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJason Miller
2nd rowLuis Garcia
3rd rowSamantha Pierce
4th rowSamantha Pierce
5th rowPriya Sharma

Common Values

ValueCountFrequency (%)
Emily Patel 1047
10.5%
Luis Garcia 1033
10.3%
Priya Sharma 1027
10.3%
David Thompson 1015
10.2%
Sophia Williams 1005
10.1%
Jason Miller 999
10.0%
Michael Chen 978
9.8%
Samantha Pierce 976
9.8%
Amit Kumar 964
9.6%
Jessica Lin 956
9.6%

Length

2025-07-17T18:35:27.718959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T18:35:27.869747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
emily 1047
 
5.2%
patel 1047
 
5.2%
luis 1033
 
5.2%
garcia 1033
 
5.2%
priya 1027
 
5.1%
sharma 1027
 
5.1%
david 1015
 
5.1%
thompson 1015
 
5.1%
sophia 1005
 
5.0%
williams 1005
 
5.0%
Other values (10) 9746
48.7%

Most occurring characters

ValueCountFrequency (%)
a 16044
13.0%
i 13999
 
11.4%
10000
 
8.1%
l 7080
 
5.8%
m 6998
 
5.7%
e 6910
 
5.6%
r 6026
 
4.9%
h 5979
 
4.9%
s 5964
 
4.8%
n 4924
 
4.0%
Other values (21) 39085
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 93009
75.6%
Uppercase Letter 20000
 
16.3%
Space Separator 10000
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 16044
17.2%
i 13999
15.1%
l 7080
7.6%
m 6998
7.5%
e 6910
7.4%
r 6026
 
6.5%
h 5979
 
6.4%
s 5964
 
6.4%
n 4924
 
5.3%
o 4034
 
4.3%
Other values (7) 15051
16.2%
Uppercase Letter
ValueCountFrequency (%)
P 3050
15.2%
S 3008
15.0%
L 1989
9.9%
M 1977
9.9%
J 1955
9.8%
E 1047
 
5.2%
G 1033
 
5.2%
T 1015
 
5.1%
D 1015
 
5.1%
W 1005
 
5.0%
Other values (3) 2906
14.5%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 113009
91.9%
Common 10000
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 16044
14.2%
i 13999
 
12.4%
l 7080
 
6.3%
m 6998
 
6.2%
e 6910
 
6.1%
r 6026
 
5.3%
h 5979
 
5.3%
s 5964
 
5.3%
n 4924
 
4.4%
o 4034
 
3.6%
Other values (20) 35051
31.0%
Common
ValueCountFrequency (%)
10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 16044
13.0%
i 13999
 
11.4%
10000
 
8.1%
l 7080
 
5.8%
m 6998
 
5.7%
e 6910
 
5.6%
r 6026
 
4.9%
h 5979
 
4.9%
s 5964
 
4.8%
n 4924
 
4.0%
Other values (21) 39085
31.8%

School_Average
Real number (ℝ)

Distinct701
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.491358
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-07-17T18:35:28.066887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.35
Q14.72
median6.51
Q38.23
95-th percentile9.66
Maximum10
Range7
Interquartile range (IQR)3.51

Descriptive statistics

Standard deviation2.0258657
Coefficient of variation (CV)0.31208658
Kurtosis-1.2071533
Mean6.491358
Median Absolute Deviation (MAD)1.76
Skewness0.0065313509
Sum64913.58
Variance4.104132
MonotonicityNot monotonic
2025-07-17T18:35:28.240797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.58 30
 
0.3%
8.21 27
 
0.3%
8.86 25
 
0.2%
5.22 25
 
0.2%
7.51 24
 
0.2%
5.25 24
 
0.2%
9.67 23
 
0.2%
7.68 23
 
0.2%
9.7 23
 
0.2%
8.74 23
 
0.2%
Other values (691) 9753
97.5%
ValueCountFrequency (%)
3 10
0.1%
3.01 16
0.2%
3.02 16
0.2%
3.03 12
0.1%
3.04 20
0.2%
3.05 11
0.1%
3.06 17
0.2%
3.07 22
0.2%
3.08 20
0.2%
3.09 14
0.1%
ValueCountFrequency (%)
10 5
 
0.1%
9.99 20
0.2%
9.98 11
0.1%
9.97 12
0.1%
9.96 11
0.1%
9.95 13
0.1%
9.94 13
0.1%
9.93 17
0.2%
9.92 18
0.2%
9.91 15
0.1%

Valuation
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size78.3 KiB

HOA
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size78.3 KiB

Rehab
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size78.3 KiB

Interactions

2025-07-17T18:35:09.523393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:50.012634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:51.886087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:53.411044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:55.008296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:56.533209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:58.071194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:59.725992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:01.579534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:03.182336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:04.728245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:06.259778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:07.970613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:09.655791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:50.295113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:51.999350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:53.533973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:55.118266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:56.649480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:58.187788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:59.838619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:01.707440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:03.298453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:04.840301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:06.375496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:08.087284image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:09.783444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:50.405034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:52.102869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:53.656968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:55.230712image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:56.761509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:58.304582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:59.952799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:01.841985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:03.412098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:04.953226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:06.491633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:08.216387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:09.927811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:50.532617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:52.229018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:53.788015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:55.359853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:56.893153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:58.434832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:00.078813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:01.982729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:03.541901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:05.079228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:06.643732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:08.352014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:10.055318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:50.645649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:52.340898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:53.908448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:55.479303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:57.008269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:58.555772image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:00.193263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:02.104020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:03.663751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:05.198287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:06.779211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:08.475564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:10.189478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:50.762268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:52.455478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:54.031768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:55.603004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:57.125281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:58.689709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:00.310743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:02.229398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:03.786374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:05.319755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:06.921425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:08.595681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:10.327934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:51.078078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:52.574664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:54.156661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:55.720898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:57.245117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:58.826482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:00.431582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:02.354214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:03.912226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:05.440051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:07.058163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:08.716536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:10.455500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:51.188601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:52.695130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:54.278132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:55.833095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:57.357198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:58.952392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:00.545693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:02.472098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:04.033449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:05.551000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:07.189876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:08.832279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:10.588689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:51.315127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:52.822865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:54.409497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:55.955992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:57.481590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:59.085463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:00.678776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:02.599263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:04.155662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:05.680006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:07.332482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:08.955894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:10.707063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:51.426366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:52.940031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:54.534123image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:56.067435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:57.610066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:59.214596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:00.796292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:02.713428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:04.267775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:05.794064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:07.461620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:09.069627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:10.827914image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:51.548181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:53.054054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:54.648663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:56.178865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:57.721608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:59.340319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:00.914725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:02.827439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:04.380282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:05.903516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:07.589036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:09.177737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:10.947115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:51.662614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:53.164153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:54.766546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:56.288611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:57.834355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:59.465612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:01.329794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:02.939874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:04.493893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:06.016906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:07.722161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:09.288894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:11.070968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:51.767690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:53.282670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:54.880974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:56.409945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:57.947598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:34:59.586058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:01.446734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:03.056277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:04.607217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:06.134636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:07.848019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-17T18:35:09.397815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-17T18:35:28.414708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
BasementYesNoBathBedCommercialFinal_ReviewerFloodHTWHighwayIRRLatitudeLayoutLongitudeMarketMost_Recent_StatusNeighborhood_RatingNet_YieldOccupancyParkingPoolProperty_TypeRent_RestrictedReviewed_StatusSQFT_BasementSQFT_MUSQFT_TotalSchool_AverageSeller_Retained_BrokerSelling_ReasonSewageSourceTax_RateTaxesTrainWaterYear_BuiltZip
BasementYesNo1.0000.0000.0080.0000.0250.0110.0000.0000.0140.0000.0190.0000.0000.0000.0000.0200.0000.0000.0080.0000.0000.0080.0230.0000.0210.0000.0000.0040.0000.0000.0000.0000.0290.0130.0110.000
Bath0.0001.0000.0040.0110.0000.0000.0000.0220.0200.0140.0000.0000.0000.0000.0000.0200.0140.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0100.0000.0180.0000.0170.0150.0000.0000.000
Bed0.0080.0041.0000.0000.0120.0000.0000.0000.0040.0050.000-0.0150.0000.0140.0000.0080.0000.0240.0080.0000.0140.000-0.0100.0040.004-0.0060.0000.0000.0270.000-0.0020.0060.0000.0000.0030.004
Commercial0.0000.0110.0001.0000.0100.0000.0220.0000.0000.0000.0360.0200.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0210.0000.0000.0000.0160.0000.0090.0020.0000.028
Final_Reviewer0.0250.0000.0120.0101.0000.0110.0000.0000.0080.0000.0220.0000.0000.0000.0000.0080.0000.0180.0260.0090.0100.0000.0130.0160.0090.0000.0000.0220.0070.0000.0000.0000.0000.0160.0020.000
Flood0.0110.0000.0000.0000.0111.0000.0000.0220.0190.0120.0180.0180.0000.0140.0000.0170.0000.0170.0000.0000.0200.0000.0090.0000.0000.0130.0000.0000.0000.0000.0000.0000.0110.0000.0120.000
HTW0.0000.0000.0000.0220.0000.0001.0000.0000.0240.0290.0000.0000.0000.0250.0420.0000.0000.0000.0060.0000.0240.0270.0000.0000.0000.0000.0000.0000.0000.0200.0360.0370.0000.0000.0190.044
Highway0.0000.0220.0000.0000.0000.0220.0001.0000.0380.0330.0000.0310.0180.0000.0100.0050.0030.0130.0000.0200.0000.0360.0000.0310.0120.0000.0000.0000.0000.0000.0000.0220.0110.0000.0000.000
IRR0.0140.0200.0040.0000.0080.0190.0240.0381.0000.0060.000-0.0030.0160.0000.000-0.0140.0000.0070.0000.0000.0140.000-0.0110.0030.0080.0050.0130.0000.0000.009-0.009-0.0110.0000.041-0.007-0.002
Latitude0.0000.0140.0050.0000.0000.0120.0290.0330.0061.0000.016-0.0100.0070.0000.0000.0100.0000.0000.0000.0000.0000.004-0.0000.002-0.0070.0250.0000.0140.0000.000-0.0340.0120.0000.012-0.004-0.013
Layout0.0190.0000.0000.0360.0220.0180.0000.0000.0000.0161.0000.0030.0000.0000.0130.0250.0270.0030.0000.0120.0160.0000.0040.0000.0160.0270.0120.0120.0050.0000.0080.0000.0000.0000.0000.000
Longitude0.0000.000-0.0150.0200.0000.0180.0000.031-0.003-0.0100.0031.0000.0000.0100.019-0.0070.0340.0110.0000.0000.0000.000-0.0110.013-0.0190.0090.0000.0000.0000.013-0.0090.0050.0280.0000.010-0.002
Market0.0000.0000.0000.0000.0000.0000.0000.0180.0160.0070.0000.0001.0000.0000.0100.0040.0000.0000.0000.0120.0000.0000.0000.0140.0000.0150.0210.0000.0060.0100.0040.0210.0000.0000.0090.000
Most_Recent_Status0.0000.0000.0140.0000.0000.0140.0250.0000.0000.0000.0000.0100.0001.0000.0000.0030.0000.0080.0220.0000.0150.0000.0060.0140.0000.0000.0410.0000.0000.0090.0140.0130.0000.0000.0090.000
Neighborhood_Rating0.0000.0000.0000.0000.0000.0000.0420.0100.0000.0000.0130.0190.0100.0001.0000.0000.0130.0000.0000.0050.0000.0000.0140.0000.0000.0210.0000.0000.0010.0000.0090.0090.0000.0070.0000.000
Net_Yield0.0200.0200.0080.0230.0080.0170.0000.005-0.0140.0100.025-0.0070.0040.0030.0001.0000.0350.0000.0280.0000.0000.0000.020-0.001-0.0210.0080.0000.0160.0000.0050.0070.0010.0000.008-0.0170.009
Occupancy0.0000.0140.0000.0000.0000.0000.0000.0030.0000.0000.0270.0340.0000.0000.0130.0351.0000.0000.0000.0000.0000.0000.0000.0170.0070.0000.0000.0000.0000.0440.0280.0170.0000.0000.0240.033
Parking0.0000.0000.0240.0000.0180.0170.0000.0130.0070.0000.0030.0110.0000.0080.0000.0000.0001.0000.0000.0200.0000.0020.0000.0210.0060.0310.0000.0000.0000.0000.0000.0000.0000.0270.0000.000
Pool0.0080.0000.0080.0000.0260.0000.0060.0000.0000.0000.0000.0000.0000.0220.0000.0280.0000.0001.0000.0000.0010.0250.0000.0070.0000.0150.0000.0160.0000.0000.0270.0000.0160.0000.0000.000
Property_Type0.0000.0070.0000.0000.0090.0000.0000.0200.0000.0000.0120.0000.0120.0000.0050.0000.0000.0200.0001.0000.0110.0150.0000.0100.0000.0140.0000.0000.0000.0100.0000.0110.0000.0000.0000.000
Rent_Restricted0.0000.0000.0140.0000.0100.0200.0240.0000.0140.0000.0160.0000.0000.0150.0000.0000.0000.0000.0010.0111.0000.0000.0000.0130.0000.0000.0080.0130.0000.0190.0000.0000.0000.0170.0000.031
Reviewed_Status0.0080.0000.0000.0000.0000.0000.0270.0360.0000.0040.0000.0000.0000.0000.0000.0000.0000.0020.0250.0150.0001.0000.0080.0090.0090.0010.0000.0000.0000.0000.0000.0000.0000.0090.0070.000
SQFT_Basement0.0230.000-0.0100.0410.0130.0090.0000.000-0.011-0.0000.004-0.0110.0000.0060.0140.0200.0000.0000.0000.0000.0000.0081.000-0.005-0.0090.0020.0000.0100.0090.0000.002-0.0180.0000.000-0.002-0.003
SQFT_MU0.0000.0000.0040.0000.0160.0000.0000.0310.0030.0020.0000.0130.0140.0140.000-0.0010.0170.0210.0070.0100.0130.009-0.0051.000-0.005-0.0050.0310.0000.0300.0050.0050.0100.0000.009-0.002-0.002
SQFT_Total0.0210.0000.0040.0000.0090.0000.0000.0120.008-0.0070.016-0.0190.0000.0000.000-0.0210.0070.0060.0000.0000.0000.009-0.009-0.0051.000-0.0020.0300.0000.0180.0110.001-0.0080.0270.026-0.006-0.013
School_Average0.0000.000-0.0060.0000.0000.0130.0000.0000.0050.0250.0270.0090.0150.0000.0210.0080.0000.0310.0150.0140.0000.0010.002-0.005-0.0021.0000.0000.0120.0070.000-0.001-0.0140.0110.027-0.0160.011
Seller_Retained_Broker0.0000.0000.0000.0210.0000.0000.0000.0000.0130.0000.0120.0000.0210.0410.0000.0000.0000.0000.0000.0000.0080.0000.0000.0310.0300.0001.0000.0000.0120.0130.0000.0160.0000.0060.0000.000
Selling_Reason0.0040.0100.0000.0000.0220.0000.0000.0000.0000.0140.0120.0000.0000.0000.0000.0160.0000.0000.0160.0000.0130.0000.0100.0000.0000.0120.0001.0000.0000.0000.0000.0000.0060.0000.0000.000
Sewage0.0000.0000.0270.0000.0070.0000.0000.0000.0000.0000.0050.0000.0060.0000.0010.0000.0000.0000.0000.0000.0000.0000.0090.0300.0180.0070.0120.0001.0000.0130.0000.0090.0230.0000.0000.011
Source0.0000.0180.0000.0000.0000.0000.0200.0000.0090.0000.0000.0130.0100.0090.0000.0050.0440.0000.0000.0100.0190.0000.0000.0050.0110.0000.0130.0000.0131.0000.0040.0000.0270.0190.0190.001
Tax_Rate0.0000.000-0.0020.0160.0000.0000.0360.000-0.009-0.0340.008-0.0090.0040.0140.0090.0070.0280.0000.0270.0000.0000.0000.0020.0050.001-0.0010.0000.0000.0000.0041.000-0.0040.0230.0000.0110.001
Taxes0.0000.0170.0060.0000.0000.0000.0370.022-0.0110.0120.0000.0050.0210.0130.0090.0010.0170.0000.0000.0110.0000.000-0.0180.010-0.008-0.0140.0160.0000.0090.000-0.0041.0000.0000.0000.0120.001
Train0.0290.0150.0000.0090.0000.0110.0000.0110.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0270.0110.0000.0060.0230.0270.0230.0001.0000.0000.0000.000
Water0.0130.0000.0000.0020.0160.0000.0000.0000.0410.0120.0000.0000.0000.0000.0070.0080.0000.0270.0000.0000.0170.0090.0000.0090.0260.0270.0060.0000.0000.0190.0000.0000.0001.0000.0000.020
Year_Built0.0110.0000.0030.0000.0020.0120.0190.000-0.007-0.0040.0000.0100.0090.0090.000-0.0170.0240.0000.0000.0000.0000.007-0.002-0.002-0.006-0.0160.0000.0000.0000.0190.0110.0120.0000.0001.0000.005
Zip0.0000.0000.0040.0280.0000.0000.0440.000-0.002-0.0130.000-0.0020.0000.0000.0000.0090.0330.0000.0000.0000.0310.000-0.003-0.002-0.0130.0110.0000.0000.0110.0010.0010.0010.0000.0200.0051.000

Missing values

2025-07-17T18:35:11.356344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-17T18:35:12.495662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-17T18:35:13.036625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Property_TitleAddressReviewed_StatusMost_Recent_StatusSourceMarketOccupancyFloodStreet_AddressCityStateZipProperty_TypeHighwayTrainTax_RateSQFT_BasementHTWPoolCommercialWaterSewageYear_BuiltSQFT_MUSQFT_TotalParkingBedBathBasementYesNoLayoutNet_YieldIRRRent_RestrictedNeighborhood_RatingLatitudeLongitudeSubdivisionTaxesSelling_ReasonSeller_Retained_BrokerFinal_ReviewerSchool_AverageValuationHOARehab
0875 Davis Overpass Suite 394, South Kathrynside, CO 35116875 Davis Overpass Suite 394, South Kathrynside, CO 35116ClosedInternalChicagoNoneNone875 Davis Overpass Suite 394South KathrynsideCO35116SFRFarNone2.95367YesNoYesWellCity192111105649Street23NoneSplit3.5314.48Yes238.415818-81.853842Manors6389DownsizingNoneJason Miller9.09[{'List_Price': 93528, 'Previous_Rent': 1699, 'ARV': 130693, 'Rent_Zestimate': 2047, 'Low_FMR': 1251, 'Redfin_Value': 379528}, {'List_Price': 281162, 'Zestimate': 522504, 'Expected_Rent': 2671, 'Low_FMR': 1306, 'High_FMR': 1859, 'Redfin_Value': 515319}, {'List_Price': 175119, 'Previous_Rent': 2188, 'Expected_Rent': 1386, 'Low_FMR': 1612}, {'List_Price': 168323, 'Zestimate': 192171, 'Expected_Rent': 3138, 'High_FMR': 5157}][{'HOA': 460, 'HOA_Flag': 'No'}, {'HOA': 476, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 66402, 'Rehab_Calculation': 54602, 'Paint': None, 'Flooring_Flag': 'No', 'Foundation_Flag': 'Yes', 'Roof_Flag': None, 'HVAC_Flag': None, 'Kitchen_Flag': None, 'Bathroom_Flag': None, 'Appliances_Flag': 'No', 'Windows_Flag': 'Yes', 'Landscaping_Flag': None, 'Trashout_Flag': 'No'}, {'Underwriting_Rehab': 20995, 'Rehab_Calculation': 67666, 'Paint': 'Yes', 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'Yes', 'Roof_Flag': 'No', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': None, 'Appliances_Flag': None, 'Windows_Flag': 'Yes', 'Landscaping_Flag': 'No', 'Trashout_Flag': 'No'}, {'Underwriting_Rehab': 64582, 'Rehab_Calculation': 68022, 'Paint': 'No', 'Flooring_Flag': None, 'Foundation_Flag': 'Yes', 'Roof_Flag': 'Yes', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': None, 'Windows_Flag': None, 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'No'}]
11159 Johnson Pass Apt. 567, South Jamesfurt, MN 603911159 Johnson Pass Apt. 567, South Jamesfurt, MN 60391ReviewedCancelledAuction.comDallasNoneMinimal Flood1159 Johnson Pass Apt. 567South JamesfurtMN60391Single Family HomeNoneFar1.75284YesNoneNoneCitySeptic199511845119Driveway22NoneColonial6.5922.92Yes335.329786-105.265714Cliff9321NoneNoneLuis Garcia7.71[{'List_Price': 434027, 'Previous_Rent': 2955, 'ARV': 187554, 'Rent_Zestimate': 3324}, {'List_Price': 298991, 'Zestimate': 391125, 'ARV': 518260, 'Rent_Zestimate': 3082, 'Low_FMR': 1728, 'Redfin_Value': 458495}, {'List_Price': 247549, 'Previous_Rent': 2412}, {'List_Price': 225463, 'Rent_Zestimate': 2004, 'Redfin_Value': 360480}][{'HOA': 322, 'HOA_Flag': 'Yes'}, {'HOA': 219, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 57763, 'Rehab_Calculation': 30244, 'Paint': 'Yes', 'Flooring_Flag': 'No', 'Foundation_Flag': None, 'Roof_Flag': None, 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'No', 'Landscaping_Flag': 'No', 'Trashout_Flag': 'Yes'}]
29082 Anna Villages Apt. 511, Port Juanshire, VI 223939082 Anna Villages Apt. 511, Port Juanshire, VI 22393NullNullOpendoorTampaNoFlood Zone9082 Anna Villages Apt. 511Port JuanshireVI22393TownhouseNoneNear2.19283NoneNoneNoneCityCity195224927606Driveway52NoRanch5.2421.17None336.793896-77.250254Square9963NoneNoneSamantha Pierce6.04[{'List_Price': 353402, 'Previous_Rent': 3478}, {'List_Price': 178184, 'Zestimate': 174118, 'Expected_Rent': 2455, 'Rent_Zestimate': 3964}, {'List_Price': 316094, 'Previous_Rent': 1855, 'ARV': 516300, 'Expected_Rent': 1577, 'Redfin_Value': 403597}][{'HOA': 242, 'HOA_Flag': 'Yes'}, {'HOA': 470, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 74265, 'Rehab_Calculation': 29580, 'Paint': None, 'Flooring_Flag': 'Yes', 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'No', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}, {'Underwriting_Rehab': 3134, 'Rehab_Calculation': 46819, 'Paint': 'No', 'Flooring_Flag': None, 'Foundation_Flag': 'No', 'Roof_Flag': 'Yes', 'HVAC_Flag': None, 'Kitchen_Flag': None, 'Bathroom_Flag': 'No', 'Appliances_Flag': 'No', 'Windows_Flag': 'Yes', 'Landscaping_Flag': None, 'Trashout_Flag': 'No'}]
386578 Lawson Park Suite 865, South Brianfurt, DC 9036686578 Lawson Park Suite 865, South Brianfurt, DC 90366ClosedCancelledM.L.S.NoneFlood Zone86578 Lawson Park Suite 865South BrianfurtDC90366DuplexNearFar3.391927NoNoneNoneCityCity193725864822None12NoRanch9.6514.12None335.456811-85.460902Springs2173Job TransferNoneSamantha Pierce3.44[{'List_Price': 149122, 'Previous_Rent': 3012, 'Zestimate': 230805, 'ARV': 437426, 'Redfin_Value': 291438}, {'List_Price': 375636, 'Rent_Zestimate': 3120, 'Low_FMR': 767, 'High_FMR': 3818}, {'List_Price': 278888, 'Previous_Rent': 3060, 'Zestimate': 441558, 'ARV': 144913, 'Expected_Rent': 3282, 'Rent_Zestimate': 3796, 'High_FMR': 4934}, {'List_Price': 268968, 'Zestimate': 388111, 'Rent_Zestimate': 946, 'Low_FMR': 1825}][{'HOA': 500, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 9019, 'Rehab_Calculation': 62585, 'Paint': None, 'Flooring_Flag': None, 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': None, 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'Yes', 'Windows_Flag': None, 'Landscaping_Flag': None, 'Trashout_Flag': None}, {'Underwriting_Rehab': 34292, 'Rehab_Calculation': 58712, 'Paint': None, 'Flooring_Flag': None, 'Foundation_Flag': 'Yes', 'Roof_Flag': None, 'HVAC_Flag': 'Yes', 'Kitchen_Flag': None, 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'No', 'Windows_Flag': 'Yes', 'Landscaping_Flag': None, 'Trashout_Flag': 'Yes'}, {'Underwriting_Rehab': 33314, 'Rehab_Calculation': 68060, 'Paint': None, 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'No', 'Roof_Flag': 'No', 'HVAC_Flag': None, 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'No', 'Windows_Flag': 'Yes', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}]
4309 Roy Brook Apt. 282, Lake Scott, FL 84478309 Roy Brook Apt. 282, Lake Scott, FL 84478ClosedCancelledM.L.S.TampaNoFlood Zone309 Roy Brook Apt. 282Lake ScottFL84478TownhouseFarNear2.201904NoneYesNoneNoneCity199112009191Garage54YesSplit2.8817.24Yes241.066408-82.300397Brooks5985Investor SaleYesPriya Sharma9.01[{'List_Price': 362688, 'Previous_Rent': 2315, 'Rent_Zestimate': 909, 'High_FMR': 4101}][{'HOA': 197, 'HOA_Flag': 'Yes'}, {'HOA': 424, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 77074, 'Rehab_Calculation': 60444, 'Paint': 'Yes', 'Flooring_Flag': 'Yes', 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': None, 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'No', 'Appliances_Flag': None, 'Windows_Flag': 'Yes', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}, {'Underwriting_Rehab': 1951, 'Rehab_Calculation': 54708, 'Paint': 'No', 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'No', 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'Yes', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'No'}, {'Underwriting_Rehab': 2974, 'Rehab_Calculation': 7901, 'Paint': 'No', 'Flooring_Flag': None, 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': None, 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': 'No', 'Trashout_Flag': None}]
5262 Smith Underpass Apt. 795, West Craig, MO 04888262 Smith Underpass Apt. 795, West Craig, MO 04888NullAlways KickBulkTampaYesFlood Zone262 Smith Underpass Apt. 795West CraigMO4888Single Family HomeNearNone2.40315YesNoneYesNoneCity197013683538None24NoneNone9.5314.41None539.720854-114.488908Mountain8242DownsizingNoneEmily Patel7.25[{'List_Price': 310609, 'Previous_Rent': 3414, 'Expected_Rent': 985, 'Low_FMR': 1932, 'High_FMR': 2584}][][{'Underwriting_Rehab': 9761, 'Rehab_Calculation': 92143, 'Paint': 'No', 'Flooring_Flag': 'Yes', 'Foundation_Flag': None, 'Roof_Flag': 'Yes', 'HVAC_Flag': 'No', 'Kitchen_Flag': None, 'Bathroom_Flag': None, 'Appliances_Flag': None, 'Windows_Flag': 'Yes', 'Landscaping_Flag': None, 'Trashout_Flag': 'No'}, {'Underwriting_Rehab': 56240, 'Rehab_Calculation': 42308, 'Paint': 'No', 'Flooring_Flag': 'No', 'Foundation_Flag': 'No', 'Roof_Flag': 'Yes', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': None, 'Appliances_Flag': 'Yes', 'Windows_Flag': 'Yes', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}, {'Underwriting_Rehab': 78898, 'Rehab_Calculation': 10597, 'Paint': 'Yes', 'Flooring_Flag': 'No', 'Foundation_Flag': 'No', 'Roof_Flag': 'Yes', 'HVAC_Flag': None, 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': None, 'Appliances_Flag': None, 'Windows_Flag': 'Yes', 'Landscaping_Flag': None, 'Trashout_Flag': 'No'}]
61150 Horn Street, Lake Nicole, WA 532341150 Horn Street, Lake Nicole, WA 53234NullNullMLSDalasYesNone1150 Horn StreetLake NicoleWA53234SFRNearNear2.881236NoneYesNoWellCity193213185782Garage23YesNone17.0316.51Yes536.944830-111.764591Coves2124Job TransferYesEmily Patel6.48[{'List_Price': 216345, 'Rent_Zestimate': 3683, 'Low_FMR': 1182, 'High_FMR': 3108}][{'HOA': 413, 'HOA_Flag': 'No'}, {'HOA': 335, 'HOA_Flag': 'No'}][{'Underwriting_Rehab': 28084, 'Rehab_Calculation': 4988, 'Paint': None, 'Flooring_Flag': None, 'Foundation_Flag': 'Yes', 'Roof_Flag': 'Yes', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': None, 'Trashout_Flag': None}, {'Underwriting_Rehab': 66027, 'Rehab_Calculation': 92696, 'Paint': 'Yes', 'Flooring_Flag': None, 'Foundation_Flag': 'Yes', 'Roof_Flag': None, 'HVAC_Flag': None, 'Kitchen_Flag': None, 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': None, 'Trashout_Flag': 'No'}, {'Underwriting_Rehab': 87660, 'Rehab_Calculation': 18589, 'Paint': None, 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'No', 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}]
7357 Huff Passage Apt. 252, Adkinsview, GU 91982357 Huff Passage Apt. 252, Adkinsview, GU 91982ClosedMLSNoFlood Zone357 Huff Passage Apt. 252AdkinsviewGU91982DuplexNoneFar1.571110YesNoneYesCityNone199723976253Street44YesSplit6.0815.27None433.458427-110.240632Mountains2964NoneYesJason Miller5.41[{'List_Price': 464598, 'ARV': 167610, 'Low_FMR': 1762, 'High_FMR': 4826}][{'HOA': 155, 'HOA_Flag': 'No'}][{'Underwriting_Rehab': 48739, 'Rehab_Calculation': 85873, 'Paint': 'No', 'Flooring_Flag': 'No', 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'No', 'Windows_Flag': 'Yes', 'Landscaping_Flag': 'No', 'Trashout_Flag': 'Yes'}, {'Underwriting_Rehab': 87969, 'Rehab_Calculation': 49852, 'Paint': 'Yes', 'Flooring_Flag': 'Yes', 'Foundation_Flag': None, 'Roof_Flag': 'Yes', 'HVAC_Flag': 'No', 'Kitchen_Flag': None, 'Bathroom_Flag': 'No', 'Appliances_Flag': None, 'Windows_Flag': None, 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'Yes'}]
848819 Morales Glen Suite 806, South Debra, PW 7148048819 Morales Glen Suite 806, South Debra, PW 71480ClosedPendBulkDalasNoneFlood Zone48819 Morales Glen Suite 806South DebraPW71480SFRNoneFar1.611081NoNoNoCityNone194810357209Garage54YesSplit14.8726.93Yes233.803862-113.575636Plain5568DownsizingYesPriya Sharma4.05[{'List_Price': 310608, 'Rent_Zestimate': 1635, 'High_FMR': 2575, 'Redfin_Value': 542460}, {'List_Price': 264059, 'Previous_Rent': 1427, 'Low_FMR': 1145, 'High_FMR': 3217, 'Redfin_Value': 249455}, {'List_Price': 409356, 'ARV': 317540, 'Redfin_Value': 119766}, {'List_Price': 360356, 'Previous_Rent': 1508, 'Zestimate': 260767, 'ARV': 155968, 'High_FMR': 2242}][][{'Underwriting_Rehab': 88098, 'Rehab_Calculation': 45361, 'Paint': None, 'Flooring_Flag': 'No', 'Foundation_Flag': 'Yes', 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}, {'Underwriting_Rehab': 28401, 'Rehab_Calculation': 46332, 'Paint': 'Yes', 'Flooring_Flag': 'Yes', 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': None, 'Bathroom_Flag': None, 'Appliances_Flag': None, 'Windows_Flag': 'No', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'No'}, {'Underwriting_Rehab': 86130, 'Rehab_Calculation': 97149, 'Paint': 'No', 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'Yes', 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'Yes', 'Windows_Flag': None, 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'Yes'}]
953324 Erin Square Suite 256, Jasminshire, NM 5533953324 Erin Square Suite 256, Jasminshire, NM 55339NullAvailableInternalChicagoNoMinimal Flood53324 Erin Square Suite 256JasminshireNM55339CondoFarNear1.14967NoNoneNoWellSeptic192024529947None62NoSplit11.3019.13Yes138.336720-116.141318Mount8007Job TransferYesJessica Lin6.68[][{'HOA': 72, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 40549, 'Rehab_Calculation': 51230, 'Paint': 'Yes', 'Flooring_Flag': None, 'Foundation_Flag': None, 'Roof_Flag': None, 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'No'}, {'Underwriting_Rehab': 29086, 'Rehab_Calculation': 52210, 'Paint': 'Yes', 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'No', 'Roof_Flag': 'Yes', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'No', 'Appliances_Flag': None, 'Windows_Flag': None, 'Landscaping_Flag': 'No', 'Trashout_Flag': 'No'}, {'Underwriting_Rehab': 23550, 'Rehab_Calculation': 13969, 'Paint': 'No', 'Flooring_Flag': None, 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'No', 'Landscaping_Flag': None, 'Trashout_Flag': None}]
Property_TitleAddressReviewed_StatusMost_Recent_StatusSourceMarketOccupancyFloodStreet_AddressCityStateZipProperty_TypeHighwayTrainTax_RateSQFT_BasementHTWPoolCommercialWaterSewageYear_BuiltSQFT_MUSQFT_TotalParkingBedBathBasementYesNoLayoutNet_YieldIRRRent_RestrictedNeighborhood_RatingLatitudeLongitudeSubdivisionTaxesSelling_ReasonSeller_Retained_BrokerFinal_ReviewerSchool_AverageValuationHOARehab
999063552 Robinson Shores Suite 521, Morrismouth, ID 3260863552 Robinson Shores Suite 521, Morrismouth, ID 32608Accepted OfferKickM L STampaYesFlood Zone63552 Robinson Shores Suite 521MorrismouthID32608TownhouseNearNear3.491330NoneNoNoNoneNone194029175262Street63YesRanch18.0120.22Yes141.739126-103.016625Terrace7768Investor SaleYesAmit Kumar6.54[{'List_Price': 296669, 'Previous_Rent': 2626, 'ARV': 122740, 'Expected_Rent': 3264, 'Rent_Zestimate': 3742, 'Low_FMR': 1757}, {'List_Price': 312434, 'Previous_Rent': 2637, 'Expected_Rent': 953, 'High_FMR': 3132}, {'List_Price': 126937, 'Zestimate': 163799, 'Expected_Rent': 3228}, {'List_Price': 135550, 'Rent_Zestimate': 972, 'Low_FMR': 1990}, {'List_Price': 273584, 'Rent_Zestimate': 1528, 'High_FMR': 4928}][{'HOA': 203, 'HOA_Flag': 'No'}, {'HOA': 331, 'HOA_Flag': 'No'}][{'Underwriting_Rehab': 45318, 'Rehab_Calculation': 18755, 'Paint': 'Yes', 'Flooring_Flag': 'No', 'Foundation_Flag': 'No', 'Roof_Flag': None, 'HVAC_Flag': None, 'Kitchen_Flag': 'No', 'Bathroom_Flag': None, 'Appliances_Flag': None, 'Windows_Flag': 'No', 'Landscaping_Flag': None, 'Trashout_Flag': 'No'}, {'Underwriting_Rehab': 38896, 'Rehab_Calculation': 67931, 'Paint': 'No', 'Flooring_Flag': 'No', 'Foundation_Flag': 'Yes', 'Roof_Flag': 'No', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': None, 'Bathroom_Flag': None, 'Appliances_Flag': None, 'Windows_Flag': 'No', 'Landscaping_Flag': 'No', 'Trashout_Flag': None}, {'Underwriting_Rehab': 6979, 'Rehab_Calculation': 5680, 'Paint': 'Yes', 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'Yes', 'Roof_Flag': None, 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'No', 'Appliances_Flag': None, 'Windows_Flag': None, 'Landscaping_Flag': 'No', 'Trashout_Flag': None}]
9991907 Hayes Park Suite 196, North Amyside, CT 19471907 Hayes Park Suite 196, North Amyside, CT 19471ClosedCancelledAuction.comTampaYesFlood Zone907 Hayes Park Suite 196North AmysideCT19471TownhouseFarFar2.811481NoneNoNoneWellCity20037507526Driveway34NoSplit12.7319.76None334.800073-98.536406Stream6176NoneNoPriya Sharma4.62[{'List_Price': 371262, 'ARV': 255590, 'Rent_Zestimate': 2996}, {'List_Price': 307132, 'Rent_Zestimate': 1074, 'Low_FMR': 1373, 'High_FMR': 4302}][{'HOA': 70, 'HOA_Flag': 'No'}, {'HOA': 492, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 85219, 'Rehab_Calculation': 98719, 'Paint': 'No', 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'Yes', 'Roof_Flag': None, 'HVAC_Flag': 'No', 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'Yes', 'Windows_Flag': None, 'Landscaping_Flag': None, 'Trashout_Flag': None}]
999229533 Rodriguez Fork Apt. 513, Lake Justin, MN 8125229533 Rodriguez Fork Apt. 513, Lake Justin, MN 81252CancelledCloseM.L.S.FloridaYesNone29533 Rodriguez Fork Apt. 513Lake JustinMN81252CondoNoneNone2.69472YesYesNoneNoneCity192726133916Street13YesSplit6.157.95Yes240.620316-109.901719Field4602Investor SaleNoneEmily Patel3.93[{'List_Price': 463853, 'Zestimate': 242359, 'Expected_Rent': 1508, 'Low_FMR': 938}, {'List_Price': 443843, 'ARV': 425532, 'Expected_Rent': 2846, 'Rent_Zestimate': 3289, 'High_FMR': 1481, 'Redfin_Value': 396776}, {'List_Price': 484171, 'Expected_Rent': 1483, 'High_FMR': 2246}, {'List_Price': 314606, 'Previous_Rent': 1763, 'Zestimate': 482338, 'Expected_Rent': 3198, 'Rent_Zestimate': 1482, 'Low_FMR': 722, 'High_FMR': 5456}][][{'Underwriting_Rehab': 55488, 'Rehab_Calculation': 63455, 'Paint': None, 'Flooring_Flag': 'Yes', 'Foundation_Flag': None, 'Roof_Flag': None, 'HVAC_Flag': None, 'Kitchen_Flag': None, 'Bathroom_Flag': 'No', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'No', 'Landscaping_Flag': 'No', 'Trashout_Flag': 'Yes'}, {'Underwriting_Rehab': 79907, 'Rehab_Calculation': 39755, 'Paint': 'No', 'Flooring_Flag': None, 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': None, 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': 'No', 'Trashout_Flag': 'Yes'}]
999380552 Watts Loaf Apt. 941, Lake Chris, MO 6263880552 Watts Loaf Apt. 941, Lake Chris, MO 62638CancelledClosedInternalTampaNoMinimal Flood80552 Watts Loaf Apt. 941Lake ChrisMO62638CondoFarNone1.461043NoneNoneNoCityCity20035978321None32NoneColonial3.2622.74No537.958583-103.838074Bypass4600Investor SaleNonePriya Sharma9.73[{'List_Price': 278307, 'Previous_Rent': 2181, 'Rent_Zestimate': 1149, 'Redfin_Value': 155137}, {'List_Price': 440833, 'ARV': 433607, 'Expected_Rent': 3116, 'Redfin_Value': 510090}, {'List_Price': 320157, 'Zestimate': 326099, 'ARV': 409348, 'Expected_Rent': 3370, 'Rent_Zestimate': 1553, 'High_FMR': 2518, 'Redfin_Value': 426274}, {'List_Price': 485516, 'Zestimate': 174707, 'Expected_Rent': 1764, 'Low_FMR': 761, 'High_FMR': 4063}][][{'Underwriting_Rehab': 98580, 'Rehab_Calculation': 11946, 'Paint': 'No', 'Flooring_Flag': 'No', 'Foundation_Flag': None, 'Roof_Flag': 'Yes', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}, {'Underwriting_Rehab': 15864, 'Rehab_Calculation': 27344, 'Paint': 'Yes', 'Flooring_Flag': 'No', 'Foundation_Flag': 'Yes', 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': None, 'Trashout_Flag': 'Yes'}, {'Underwriting_Rehab': 87938, 'Rehab_Calculation': 47288, 'Paint': None, 'Flooring_Flag': 'No', 'Foundation_Flag': 'Yes', 'Roof_Flag': 'No', 'HVAC_Flag': None, 'Kitchen_Flag': None, 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'Yes', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'Yes'}]
9994892 Reid Route, Port Sarah, VA 72701892 Reid Route, Port Sarah, VA 72701ReviewedKickM.L.S.FloridaNoFlood Zone892 Reid RoutePort SarahVA72701CondoNearNear1.37648NoneNoneNoneWellSeptic195024125974Garage33NoneSplit13.3712.85Yes540.190785-100.198377Centers5084Investor SaleNoneJason Miller3.09[][{'HOA': 201, 'HOA_Flag': 'No'}, {'HOA': 5, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 12180, 'Rehab_Calculation': 19369, 'Paint': None, 'Flooring_Flag': None, 'Foundation_Flag': 'Yes', 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'No', 'Windows_Flag': 'No', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'Yes'}, {'Underwriting_Rehab': 48037, 'Rehab_Calculation': 76810, 'Paint': None, 'Flooring_Flag': 'Yes', 'Foundation_Flag': None, 'Roof_Flag': 'Yes', 'HVAC_Flag': 'No', 'Kitchen_Flag': None, 'Bathroom_Flag': None, 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'Yes'}]
9995139 Fisher Spur, North Brandon, WA 23114139 Fisher Spur, North Brandon, WA 23114OfferedPendingBulkChicgoNoNone139 Fisher SpurNorth BrandonWA23114Single Family HomeNoneNear3.34773NoneNoneNoWellSeptic201425586372None44YesColonial17.2824.05Yes541.585551-84.880198Curve4822NoneYesDavid Thompson3.08[{'List_Price': 408947, 'High_FMR': 5480}, {'List_Price': 366647, 'ARV': 173130, 'Expected_Rent': 3316, 'Low_FMR': 972, 'High_FMR': 2061}, {'List_Price': 275256, 'Zestimate': 190338, 'ARV': 141971, 'Expected_Rent': 2406, 'Redfin_Value': 242447}][{'HOA': 417, 'HOA_Flag': 'No'}][{'Underwriting_Rehab': 49365, 'Rehab_Calculation': 87913, 'Paint': 'No', 'Flooring_Flag': 'Yes', 'Foundation_Flag': None, 'Roof_Flag': None, 'HVAC_Flag': 'No', 'Kitchen_Flag': None, 'Bathroom_Flag': 'No', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': 'No', 'Trashout_Flag': 'Yes'}, {'Underwriting_Rehab': 6954, 'Rehab_Calculation': 70817, 'Paint': None, 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'No', 'Roof_Flag': None, 'HVAC_Flag': 'No', 'Kitchen_Flag': None, 'Bathroom_Flag': 'No', 'Appliances_Flag': None, 'Windows_Flag': None, 'Landscaping_Flag': None, 'Trashout_Flag': None}, {'Underwriting_Rehab': 46397, 'Rehab_Calculation': 71455, 'Paint': None, 'Flooring_Flag': 'No', 'Foundation_Flag': 'Yes', 'Roof_Flag': 'No', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': None, 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'No', 'Landscaping_Flag': None, 'Trashout_Flag': 'No'}]
9996275 Gutierrez Views, Lake Victoria, WY 63275275 Gutierrez Views, Lake Victoria, WY 63275ReviewedCloseM L SDalasYesFlood Zone275 Gutierrez ViewsLake VictoriaWY63275DuplexNearNone3.181440NoneNoneYesNoneSeptic200121775690None22NoneColonial10.4117.38No434.402968-112.249250Field4850NoneNoneSophia Williams9.88[{'List_Price': 183622, 'Previous_Rent': 1938, 'ARV': 399912, 'Rent_Zestimate': 3914, 'Low_FMR': 1996, 'High_FMR': 1969}, {'List_Price': 276522, 'Previous_Rent': 953, 'Rent_Zestimate': 3870, 'Low_FMR': 1154, 'Redfin_Value': 585631}, {'List_Price': 241438, 'Previous_Rent': 2598, 'Low_FMR': 1818, 'Redfin_Value': 161831}][{'HOA': 103, 'HOA_Flag': 'No'}, {'HOA': 334, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 29448, 'Rehab_Calculation': 16528, 'Paint': 'No', 'Flooring_Flag': 'No', 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'No', 'Windows_Flag': 'No', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}, {'Underwriting_Rehab': 61695, 'Rehab_Calculation': 89482, 'Paint': 'No', 'Flooring_Flag': 'No', 'Foundation_Flag': 'No', 'Roof_Flag': 'No', 'HVAC_Flag': None, 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'Yes', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'No', 'Landscaping_Flag': None, 'Trashout_Flag': 'Yes'}]
9997341 Vanessa Harbor, North Melissa, CT 39313341 Vanessa Harbor, North Melissa, CT 39313NullAlways KickBulkTampaNoneFlood Zone341 Vanessa HarborNorth MelissaCT39313Single Family HomeFarNear2.941900NoneYesNoWellSeptic200310798538Street23NoSplit13.4912.07No439.496975-103.962353Junction8370NoneYesJason Miller7.74[{'List_Price': 477121, 'Zestimate': 215341, 'Expected_Rent': 1161, 'Rent_Zestimate': 1546, 'Low_FMR': 1054}, {'List_Price': 159043, 'Previous_Rent': 2011, 'Zestimate': 456135, 'ARV': 112129, 'Expected_Rent': 2921, 'Low_FMR': 926}][][{'Underwriting_Rehab': 63941, 'Rehab_Calculation': 82902, 'Paint': 'Yes', 'Flooring_Flag': None, 'Foundation_Flag': None, 'Roof_Flag': 'No', 'HVAC_Flag': 'Yes', 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'No', 'Appliances_Flag': None, 'Windows_Flag': 'No', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}]
999878647 Brittany Village, North Ethanborough, NY 2136278647 Brittany Village, North Ethanborough, NY 21362OfferedCancelMLSChicagoNoneMinimal Flood78647 Brittany VillageNorth EthanboroughNY21362Single Family HomeNearNear1.28570NoneNoNoCityNone20065996561Garage13NoneNone2.6519.47None141.295438-104.227932Locks9005Investor SaleNoSamantha Pierce6.62[{'List_Price': 137917, 'Previous_Rent': 2079, 'ARV': 260738, 'Expected_Rent': 2762, 'Rent_Zestimate': 2318, 'Low_FMR': 884}, {'List_Price': 154755, 'Zestimate': 232506, 'Redfin_Value': 134958}, {'List_Price': 427393, 'ARV': 287444, 'Rent_Zestimate': 1640, 'Redfin_Value': 166012}][{'HOA': 461, 'HOA_Flag': 'Yes'}, {'HOA': 469, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 34520, 'Rehab_Calculation': 2731, 'Paint': None, 'Flooring_Flag': None, 'Foundation_Flag': 'Yes', 'Roof_Flag': None, 'HVAC_Flag': 'No', 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': 'Yes', 'Trashout_Flag': None}, {'Underwriting_Rehab': 77901, 'Rehab_Calculation': 32822, 'Paint': 'Yes', 'Flooring_Flag': None, 'Foundation_Flag': 'No', 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'Yes', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'No', 'Landscaping_Flag': 'No', 'Trashout_Flag': 'Yes'}, {'Underwriting_Rehab': 5961, 'Rehab_Calculation': 26625, 'Paint': 'Yes', 'Flooring_Flag': 'Yes', 'Foundation_Flag': 'Yes', 'Roof_Flag': 'Yes', 'HVAC_Flag': None, 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'Yes', 'Windows_Flag': 'No', 'Landscaping_Flag': 'Yes', 'Trashout_Flag': 'Yes'}]
99995461 Nichols Throughway, East Crystalborough, UT 084975461 Nichols Throughway, East Crystalborough, UT 08497CancelledCancelledM L STampaYesFlood Zone5461 Nichols ThroughwayEast CrystalboroughUT8497Single Family HomeFarFar3.391981NoNoYesWellSeptic195615447343Garage41NoColonial5.4628.37No338.546763-80.907870Light5199DownsizingYesEmily Patel3.73[][{'HOA': 18, 'HOA_Flag': 'No'}, {'HOA': 170, 'HOA_Flag': 'Yes'}][{'Underwriting_Rehab': 55645, 'Rehab_Calculation': 16508, 'Paint': None, 'Flooring_Flag': None, 'Foundation_Flag': 'Yes', 'Roof_Flag': 'No', 'HVAC_Flag': 'No', 'Kitchen_Flag': 'No', 'Bathroom_Flag': 'No', 'Appliances_Flag': 'No', 'Windows_Flag': None, 'Landscaping_Flag': None, 'Trashout_Flag': 'No'}]